advantages of hypothesis in social research

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8 Advantages of the Hypothesis

• Published: October 2019
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This chapter makes the case for the scientific hypothesis from two quite different points of view: statistical and cognitive. The consideration of statistical advantages picks up from the discussion of the Reproducibility Crisis in the previous chapter. First, it explores reasoning that shows that hypothesis-based research will, as a general rule, be much more reliable than, for example, open-ended gene searches. It also revives a procedure, Fisher’s Method for Combining Results that, though rarely used nowadays, underscores the strengths of multiple testing of hypotheses. Second, the chapter goes into many cognitive advantages of hypothesis-based research that exist because the human mind is inherently and continually at work trying to understand the world. The hypothesis is a natural way of channeling this drive into science. It is also a powerful organizational tool that serves as a blueprint for investigations and helps organize scientific thinking and communications.

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• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Developing a Research Question

18 Hypotheses

When researchers do not have predictions about what they will find, they conduct research to answer a question or questions, with an open-minded desire to know about a topic, or to help develop hypotheses for later testing. In other situations, the purpose of research is to test a specific hypothesis or hypotheses.  A hypothesis is a statement, sometimes but not always causal, describing a researcher’s expectations regarding anticipated finding. Often hypotheses are written to describe the expected relationship between two variables (though this is not a requirement). To develop a hypothesis, one needs to understand the differences between independent and dependent variables and between units of observation and units of analysis. Hypotheses are typically drawn from theories and usually describe how an independent variable is expected to affect some dependent variable or variables. Researchers following a deductive approach to their research will hypothesize about what they expect to find based on the theory or theories that frame their study. If the theory accurately reflects the phenomenon it is designed to explain, then the researcher’s hypotheses about what would be observed in the real world should bear out.

Sometimes researchers will hypothesize that a relationship will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and legalization of marijuana. Perhaps you have done some reading in your spare time, or in another course you have taken.  Based on the theories you have read, you hypothesize that “age is negatively related to support for marijuana legalization.” What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their support for marijuana legalization decreases. Thus, as age moves in one direction (up), support for marijuana legalization moves in another direction (down). If writing hypotheses feels tricky, it is sometimes helpful to draw them out. and depict each of the two hypotheses we have just discussed.

Note that you will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a relationship has been shown to exist with absolute certainty and that there is no chance that there are conditions under which the hypothesis would not bear out. Instead, researchers tend to say that their hypotheses have been supported (or not) . This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining a relationship will be discovered. Researchers may also discuss a null hypothesis, one that predicts no relationship between the variables being studied. If a researcher rejects the null hypothesis, he or she is saying that the variables in question are somehow related to one another.

Quantitative and qualitative researchers tend to take different approaches when it comes to hypotheses. In quantitative research, the goal often is to empirically test hypotheses generated from theory. With a qualitative approach, on the other hand, a researcher may begin with some vague expectations about what he or she will find, but the aim is not to test one’s expectations against some empirical observations. Instead, theory development or construction is the goal. Qualitative researchers may develop theories from which hypotheses can be drawn and quantitative researchers may then test those hypotheses. Both types of research are crucial to understanding our social world, and both play an important role in the matter of hypothesis development and testing.  In the following section, we will look at qualitative and quantitative approaches to research, as well as mixed methods.

Text Attributions

• This chapter has been adapted from Chapter 5.2 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License .

An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Practice  Questions  – Write short note on Importance and Sources of Hypothesis in Sociological Research. [ UPSC 2008]

Approach –  Introduction, What makes Hypothesis relevant in a sociological research?, What are the sources which aids us to derive hypothesis?, Conclusion

INTRODUCTION

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

We know that research begins with a problem or a felt need or difficulty. The purpose of research is to find a solution to the difficulty. It is desirable that the researcher should propose a set of suggested solutions or explanations of the  difficulty which the research proposes to solve. Such tentative solutions formulated as a proposition are called hypotheses. The suggested solutions formulated as hypotheses may or may not be the real solutions to the problem. Whether they are or not is the task of research to test and establish.

DEFINTITIONS

• Lundberg- A Hypothesis is a tentative generalisation, the validity of which remains to be tested. In its most elementary stages, the hypothesis may be any hunch, guess imaginative idea or Intuition whatsoever which becomes the basis of action or Investigation.
• Bogardus- A Hypothesis is a proposition to be tested.
• Goode and Hatt- It is a proposition which can be put to test to determinants validity.
• P. V. Yaung- The idea of ​a temporary but central importance that becomes the basis of useful research is called a working hypothesis.

TYPES OF HYPOTHESIS

i)  Explanatory Hypothesis : The purpose of this hypothesis is to explain a certain fact. All hypotheses are in a way explanatory for a hypothesis is advanced only when we try to explain the observed fact. A large number of hypotheses are advanced to explain the individual facts in life. A theft, a murder, an accident are examples.

ii) Descriptive Hypothesis:  Some times a researcher comes across a complex phenomenon. He/ she does not understand the relations among the observed facts. But how to account for these facts? The answer is a descriptive hypothesis. A hypothesis is descriptive when it is based upon the points of resemblance of some thing. It describes the cause and effect relationship of a phenomenon e.g., the current unemployment rate of a state exceeds 25% of the work force. Similarly, the consumers of local made products constitute asignificant market segment.

iii) Analogical Hypothesis : When we formulate a hypothesis on the basis of similarities (analogy), it is called an analogical hypothesis e.g., families with higher earnings invest more surplus income on long term investments.

iv) Working hypothesis : Some times certain facts cannot be explained adequately by existing hypotheses, and no new hypothesis comes up. Thus, the investigation is held up. In this situation, a researcher formulates a hypothesis which enables to continue investigation. Such a hypothesis, though inadequate and formulated for the purpose of further investigation only, is called a working hypothesis. It is simply accepted as a starting point in the process of investigation.

v) Null Hypothesis:  It is an important concept that is used widely in the sampling theory. It forms the basis of many tests of significance. Under this type, the hypothesis is stated negatively. It is null because it may be nullified, if the evidence of a random sample is unfavourable to the hypothesis. It is a hypothesis being tested (H0). If the calculated value of the test is less than the permissible value, Null hypothesis is accepted, otherwise it is rejected. The rejection of a null hypothesis implies that the difference could not have arisen due to chance or sampling fluctuations.

USES OF HYPOTHESIS

i) It is a starting point for many a research work. ii) It helps in deciding the direction in which to proceed. iii) It helps in selecting and collecting pertinent facts. iv) It is an aid to explanation. v) It helps in drawing specific conclusions. vi) It helps in testing theories. vii) It works as a basis for future knowledge.

ROLE  OF HYPOTHESIS

In any scientific investigation, the role of hypothesis is indispensable as it always guides and gives direction to scientific research. Research remains unfocused without a hypothesis. Without it, the scientist is not in position to decide as to what to observe and how to observe. He may at best beat around the bush. In the words of Northrop, “The function of hypothesis is to direct our search for order among facts, the suggestions formulated in any hypothesis may be solution to the problem, whether they are, is the task of the enquiry”.

First ,  it is an operating tool of theory. It can be deduced from other hypotheses and theories. If it is correctly drawn and scientifically formulated, it enables the researcher to proceed on correct line of study. Due to this progress, the investigator becomes capable of drawing proper conclusions. In the words of Goode and Hatt, “without hypothesis the research is unfocussed, a random empirical wandering. The results cannot be studied as facts with clear meaning. Hypothesis is a necessary link between theory and investigation which leads to discovery and addition to knowledge.

Secondly,  the hypothesis acts as a pointer to enquiry. Scientific research has to proceed in certain definite lines and through hypothesis the researcher becomes capable of knowing specifically what he has to find out by determining the direction provided by the hypothesis. Hypotheses acts like a pole star or a compass to a sailor with the help of which he is able to head in the proper direction.

Thirdly , the hypothesis enables us to select relevant and pertinent facts and makes our task easier. Once, the direction and points are identified, the researcher is in a position to eliminate the irrelevant facts and concentrate only on the relevant facts. Highlighting the role of hypothesis in providing pertinent facts, P.V. Young has stated, “The use of hypothesis prevents a blind research and indiscriminate gathering of masses of data which may later prove irrelevant to the problem under study”. For example, if the researcher is interested in examining the relationship between broken home and juvenile delinquency, he can easily proceed in the proper direction and collect pertinent information succeeded only when he has succeed in formulating a useful hypothesis.

Fourthly , the hypothesis provides guidance by way of providing the direction, pointing to enquiry, enabling to select pertinent facts and helping to draw specific conclusions. It saves the researcher from the botheration of ‘trial and error’ which causes loss of money, energy and time.

Finally,  the hypothesis plays a significant role in facilitating advancement of knowledge beyond one’s value and opinions. In real terms, the science is incomplete without hypotheses.

STAGES OF HYPOTHESIS TESTING

• EXPERIMENTATION   : Research study focuses its study which is manageable and approachable to it and where it can test its hypothesis. The study gradually becomes more focused on its variables and influences on variables so that hypothesis may be tested. In this process, hypothesis can be disproved.
• REHEARSAL TESTING :   The researcher should conduct a pre testing or rehearsal before going for field work or data collection.
• FIELD RESEARCH :  To test and investigate hypothesis, field work with predetermined research methodology tools is conducted in which interviews, observations with stakeholders, questionnaires, surveys etc are used to follow. The documentation study may also happens at this stage.
• PRIMARY & SECONDARY DATA/INFORMATION ANALYSIS :  The primary or secondary data and information’s available prior to hypothesis testing may be used to ascertain validity of hypothesis itself.

Formulating a hypothesis can take place at the very beginning of a research project, or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis. Whenever a hypothesis is formulated, the most important thing is to be precise about what one’s variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

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Hypothesis: Functions, Problems, Types, Characteristics, Examples

Basic Elements of the Scientific Method: Hypotheses

The Function of the Hypotheses

A hypothesis states what one is looking for in an experiment. When facts are assembled, ordered, and seen in a relationship, they build up to become a theory. This theory needs to be deduced for further confirmation of the facts, this formulation of the deductions constitutes of a hypothesis. As a theory states a logical relationship between facts and from this, the propositions which are deduced should be true. Hence, these deduced prepositions are called hypotheses.

Problems in Formulating the Hypothesis

There are three major difficulties in the formulation of a hypothesis, they are as follows:

Sometimes the deduction of a hypothesis may be difficult as there would be many variables and the necessity to take them all into consideration becomes a challenge. For instance, observing two cases:

Deduction: This situation holds much more sense to the people who are in professions such as psychotherapy, psychiatry and law to some extent. They possess a very intimate relationship with their clients, thus are more susceptible to issues regarding emotional strains in the client-practitioner relationship and more implicit and explicit controls over both participants in comparison to other professions.

2. Principle: Extensive but relatively systematized data show the correlation between members of the upper occupational class and less unhappiness and worry. Also, they are subjected to more formal controls than members of the lower strata.

Deduction: There can numerous ways to approach this principle, one could go with the comparison applying to martial relationships of the members and further argue that such differential pressures could be observed through divorce rates. This hypothesis would show inverse correlations between class position and divorce rates. There would be a very strong need to define the terms carefully to show the deduction from the principle problem.

Types of Hypothesis

There are many ways to classify hypotheses, but it seems adequate to distinguish to separate them on the basis of their level of abstraction. They can be divided into three broad levels which will be increasing in abstractness.

Science and Hypothesis

“The general culture in which a science develops furnishes many of its basic hypotheses” holds true as science has developed more in the West and is no accident that it is a function of culture itself. This is quite evident with the culture of the West as they read for morals, science and happiness. After the examination of a bunch of variables, it is quite easy to say that the cultural emphasis upon happiness has been productive of an almost limitless range.

Analogies are a source of useful hypotheses but not without its dangers as all variables may not be accounted for it as no civilization has a perfect system.

Hypotheses are also the consequence of personal, idiosyncratic experience as the manner in which the individual reacts to the hypotheses is also important and should be accounted for in the experiment.

The Characteristics for Usable Hypotheses

Also Read: Research Methods – Basics

Goode, W. E. and P. K. Hatt. 1952. Methods in Social Research.New York: McGraw Hill. Chapters 5 and 6. Pp. 41-73

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1.3 Conducting Research in Social Psychology

Learning objectives.

• Explain why social psychologists rely on empirical methods to study social behavior.
• Provide examples of how social psychologists measure the variables they are interested in.
• Review the three types of research designs, and evaluate the strengths and limitations of each type.
• Consider the role of validity in research, and describe how research programs should be evaluated.

Social psychologists are not the only people interested in understanding and predicting social behavior or the only people who study it. Social behavior is also considered by religious leaders, philosophers, politicians, novelists, and others, and it is a common topic on TV shows. But the social psychological approach to understanding social behavior goes beyond the mere observation of human actions. Social psychologists believe that a true understanding of the causes of social behavior can only be obtained through a systematic scientific approach, and that is why they conduct scientific research. Social psychologists believe that the study of social behavior should be empirical —that is, based on the collection and systematic analysis of observable data .

The Importance of Scientific Research

Because social psychology concerns the relationships among people, and because we can frequently find answers to questions about human behavior by using our own common sense or intuition, many people think that it is not necessary to study it empirically (Lilienfeld, 2011). But although we do learn about people by observing others and therefore social psychology is in fact partly common sense, social psychology is not entirely common sense.

In case you are not convinced about this, perhaps you would be willing to test whether or not social psychology is just common sense by taking a short true-or-false quiz. If so, please have a look at Table 1.1 “Is Social Psychology Just Common Sense?” and respond with either “True” or “False.” Based on your past observations of people’s behavior, along with your own common sense, you will likely have answers to each of the questions on the quiz. But how sure are you? Would you be willing to bet that all, or even most, of your answers have been shown to be correct by scientific research? Would you be willing to accept your score on this quiz for your final grade in this class? If you are like most of the students in my classes, you will get at least some of these answers wrong. (To see the answers and a brief description of the scientific research supporting each of these topics, please go to the Chapter Summary at the end of this chapter.)

Table 1.1 Is Social Psychology Just Common Sense?

One of the reasons we might think that social psychology is common sense is that once we learn about the outcome of a given event (e.g., when we read about the results of a research project), we frequently believe that we would have been able to predict the outcome ahead of time. For instance, if half of a class of students is told that research concerning attraction between people has demonstrated that “opposites attract,” and if the other half is told that research has demonstrated that “birds of a feather flock together,” most of the students in both groups will report believing that the outcome is true and that they would have predicted the outcome before they had heard about it. Of course, both of these contradictory outcomes cannot be true. The problem is that just reading a description of research findings leads us to think of the many cases that we know that support the findings and thus makes them seem believable. The tendency to think that we could have predicted something that we probably would not have been able to predict is called the hindsight bias .

Our common sense also leads us to believe that we know why we engage in the behaviors that we engage in, when in fact we may not. Social psychologist Daniel Wegner and his colleagues have conducted a variety of studies showing that we do not always understand the causes of our own actions. When we think about a behavior before we engage in it, we believe that the thinking guided our behavior, even when it did not (Morewedge, Gray, & Wegner, 2010). People also report that they contribute more to solving a problem when they are led to believe that they have been working harder on it, even though the effort did not increase their contribution to the outcome (Preston & Wegner, 2007). These findings, and many others like them, demonstrate that our beliefs about the causes of social events, and even of our own actions, do not always match the true causes of those events.

Social psychologists conduct research because it often uncovers results that could not have been predicted ahead of time. Putting our hunches to the test exposes our ideas to scrutiny. The scientific approach brings a lot of surprises, but it also helps us test our explanations about behavior in a rigorous manner. It is important for you to understand the research methods used in psychology so that you can evaluate the validity of the research that you read about here, in other courses, and in your everyday life.

Social psychologists publish their research in scientific journals, and your instructor may require you to read some of these research articles. The most important social psychology journals are listed in Table 1.2 “Social Psychology Journals” . If you are asked to do a literature search on research in social psychology, you should look for articles from these journals.

Table 1.2 Social Psychology Journals

We’ll discuss the empirical approach and review the findings of many research projects throughout this book, but for now let’s take a look at the basics of how scientists use research to draw overall conclusions about social behavior. Keep in mind as you read this book, however, that although social psychologists are pretty good at understanding the causes of behavior, our predictions are a long way from perfect. We are not able to control the minds or the behaviors of others or to predict exactly what they will do in any given situation. Human behavior is complicated because people are complicated and because the social situations that they find themselves in every day are also complex. It is this complexity—at least for me—that makes studying people so interesting and fun.

Measuring Affect, Behavior, and Cognition

One important aspect of using an empirical approach to understand social behavior is that the concepts of interest must be measured ( Figure 1.4 “The Operational Definition” ). If we are interested in learning how much Sarah likes Robert, then we need to have a measure of her liking for him. But how, exactly, should we measure the broad idea of “liking”? In scientific terms, the characteristics that we are trying to measure are known as conceptual variables , and the particular method that we use to measure a variable of interest is called an operational definition .

For anything that we might wish to measure, there are many different operational definitions, and which one we use depends on the goal of the research and the type of situation we are studying. To better understand this, let’s look at an example of how we might operationally define “Sarah likes Robert.”

Figure 1.4 The Operational Definition

An idea or conceptual variable (such as “how much Sarah likes Robert”) is turned into a measure through an operational definition.

One approach to measurement involves directly asking people about their perceptions using self-report measures. Self-report measures are measures in which individuals are asked to respond to questions posed by an interviewer or on a questionnaire . Generally, because any one question might be misunderstood or answered incorrectly, in order to provide a better measure, more than one question is asked and the responses to the questions are averaged together. For example, an operational definition of Sarah’s liking for Robert might involve asking her to complete the following measure:

I enjoy being around Robert.

Strongly disagree 1 2 3 4 5 6 Strongly agree

I get along well with Robert.

I like Robert.

The operational definition would be the average of her responses across the three questions. Because each question assesses the attitude differently, and yet each question should nevertheless measure Sarah’s attitude toward Robert in some way, the average of the three questions will generally be a better measure than would any one question on its own.

Although it is easy to ask many questions on self-report measures, these measures have a potential disadvantage. As we have seen, people’s insights into their own opinions and their own behaviors may not be perfect, and they might also not want to tell the truth—perhaps Sarah really likes Robert, but she is unwilling or unable to tell us so. Therefore, an alternative to self-report that can sometimes provide a more valid measure is to measure behavior itself. Behavioral measures are measures designed to directly assess what people do . Instead of asking Sara how much she likes Robert, we might instead measure her liking by assessing how much time she spends with Robert or by coding how much she smiles at him when she talks to him. Some examples of behavioral measures that have been used in social psychological research are shown in Table 1.3 “Examples of Operational Definitions of Conceptual Variables That Have Been Used in Social Psychological Research” .

Table 1.3 Examples of Operational Definitions of Conceptual Variables That Have Been Used in Social Psychological Research

Social Neuroscience: Measuring Social Responses in the Brain

Still another approach to measuring our thoughts and feelings is to measure brain activity, and recent advances in brain science have created a wide variety of new techniques for doing so. One approach, known as electroencephalography (EEG) , is a technique that records the electrical activity produced by the brain’s neurons through the use of electrodes that are placed around the research participant’s head . An electroencephalogram (EEG) can show if a person is asleep, awake, or anesthetized because the brain wave patterns are known to differ during each state. An EEG can also track the waves that are produced when a person is reading, writing, and speaking with others. A particular advantage of the technique is that the participant can move around while the recordings are being taken, which is useful when measuring brain activity in children who often have difficulty keeping still. Furthermore, by following electrical impulses across the surface of the brain, researchers can observe changes over very fast time periods.

This woman is wearing an EEG cap.

goocy – Research – CC BY-NC 2.0.

Although EEGs can provide information about the general patterns of electrical activity within the brain, and although they allow the researcher to see these changes quickly as they occur in real time, the electrodes must be placed on the surface of the skull, and each electrode measures brain waves from large areas of the brain. As a result, EEGs do not provide a very clear picture of the structure of the brain.

But techniques exist to provide more specific brain images. Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that uses a magnetic field to create images of brain structure and function . In research studies that use the fMRI, the research participant lies on a bed within a large cylindrical structure containing a very strong magnet. Nerve cells in the brain that are active use more oxygen, and the need for oxygen increases blood flow to the area. The fMRI detects the amount of blood flow in each brain region and thus is an indicator of which parts of the brain are active.

Very clear and detailed pictures of brain structures (see Figure 1.5 “Functional Magnetic Resonance Imaging (fMRI)” ) can be produced via fMRI. Often, the images take the form of cross-sectional “slices” that are obtained as the magnetic field is passed across the brain. The images of these slices are taken repeatedly and are superimposed on images of the brain structure itself to show how activity changes in different brain structures over time. Normally, the research participant is asked to engage in tasks while in the scanner, for instance, to make judgments about pictures of people, to solve problems, or to make decisions about appropriate behaviors. The fMRI images show which parts of the brain are associated with which types of tasks. Another advantage of the fMRI is that is it noninvasive. The research participant simply enters the machine and the scans begin.

Figure 1.5 Functional Magnetic Resonance Imaging (fMRI)

The fMRI creates images of brain structure and activity. In this image, the red and yellow areas represent increased blood flow and thus increased activity.

Reigh LeBlanc – Reigh’s Brain rlwat – CC BY-NC 2.0; Wikimedia Commons – public domain.

Although the scanners themselves are expensive, the advantages of fMRIs are substantial, and scanners are now available in many university and hospital settings. The fMRI is now the most commonly used method of learning about brain structure, and it has been employed by social psychologists to study social cognition, attitudes, morality, emotions, responses to being rejected by others, and racial prejudice, to name just a few topics (Eisenberger, Lieberman, & Williams, 2003; Greene, Sommerville, Nystrom, Darley, & Cohen, 2001; Lieberman, Hariri, Jarcho, Eisenberger, & Bookheimer, 2005; Ochsner, Bunge, Gross, & Gabrieli, 2002; Richeson et al., 2003).

Observational Research

Once we have decided how to measure our variables, we can begin the process of research itself. As you can see in Table 1.4 “Three Major Research Designs Used by Social Psychologists” , there are three major approaches to conducting research that are used by social psychologists—the observational approach , the correlational approach , and the experimental approach . Each approach has some advantages and disadvantages.

Table 1.4 Three Major Research Designs Used by Social Psychologists

The most basic research design, observational research , is research that involves making observations of behavior and recording those observations in an objective manner . Although it is possible in some cases to use observational data to draw conclusions about the relationships between variables (e.g., by comparing the behaviors of older versus younger children on a playground), in many cases the observational approach is used only to get a picture of what is happening to a given set of people at a given time and how they are responding to the social situation. In these cases, the observational approach involves creating a type of “snapshot” of the current state of affairs.

One advantage of observational research is that in many cases it is the only possible approach to collecting data about the topic of interest. A researcher who is interested in studying the impact of a hurricane on the residents of New Orleans, the reactions of New Yorkers to a terrorist attack, or the activities of the members of a religious cult cannot create such situations in a laboratory but must be ready to make observations in a systematic way when such events occur on their own. Thus observational research allows the study of unique situations that could not be created by the researcher. Another advantage of observational research is that the people whose behavior is being measured are doing the things they do every day, and in some cases they may not even know that their behavior is being recorded.

One early observational study that made an important contribution to understanding human behavior was reported in a book by Leon Festinger and his colleagues (Festinger, Riecken, & Schachter, 1956). The book, called When Prophecy Fails , reported an observational study of the members of a “doomsday” cult. The cult members believed that they had received information, supposedly sent through “automatic writing” from a planet called “Clarion,” that the world was going to end. More specifically, the group members were convinced that the earth would be destroyed, as the result of a gigantic flood, sometime before dawn on December 21, 1954.

When Festinger learned about the cult, he thought that it would be an interesting way to study how individuals in groups communicate with each other to reinforce their extreme beliefs. He and his colleagues observed the members of the cult over a period of several months, beginning in July of the year in which the flood was expected. The researchers collected a variety of behavioral and self-report measures by observing the cult, recording the conversations among the group members, and conducting detailed interviews with them. Festinger and his colleagues also recorded the reactions of the cult members, beginning on December 21, when the world did not end as they had predicted. This observational research provided a wealth of information about the indoctrination patterns of cult members and their reactions to disconfirmed predictions. This research also helped Festinger develop his important theory of cognitive dissonance.

Despite their advantages, observational research designs also have some limitations. Most important, because the data that are collected in observational studies are only a description of the events that are occurring, they do not tell us anything about the relationship between different variables. However, it is exactly this question that correlational research and experimental research are designed to answer.

The Research Hypothesis

Because social psychologists are generally interested in looking at relationships among variables, they begin by stating their predictions in the form of a precise statement known as a research hypothesis . A research hypothesis is a statement about the relationship between the variables of interest and about the specific direction of that relationship . For instance, the research hypothesis “People who are more similar to each other will be more attracted to each other” predicts that there is a relationship between a variable called similarity and another variable called attraction. In the research hypothesis “The attitudes of cult members become more extreme when their beliefs are challenged,” the variables that are expected to be related are extremity of beliefs and the degree to which the cults’ beliefs are challenged.

Because the research hypothesis states both that there is a relationship between the variables and the direction of that relationship, it is said to be falsifiable . Being falsifiable means that the outcome of the research can demonstrate empirically either that there is support for the hypothesis (i.e., the relationship between the variables was correctly specified) or that there is actually no relationship between the variables or that the actual relationship is not in the direction that was predicted . Thus the research hypothesis that “people will be more attracted to others who are similar to them” is falsifiable because the research could show either that there was no relationship between similarity and attraction or that people we see as similar to us are seen as less attractive than those who are dissimilar.

Correlational Research

The goal of correlational research is to search for and test hypotheses about the relationships between two or more variables. In the simplest case, the correlation is between only two variables, such as that between similarity and liking, or between gender (male versus female) and helping.

In a correlational design, the research hypothesis is that there is an association (i.e., a correlation) between the variables that are being measured. For instance, many researchers have tested the research hypothesis that a positive correlation exists between the use of violent video games and the incidence of aggressive behavior, such that people who play violent video games more frequently would also display more aggressive behavior.

A statistic known as the Pearson correlation coefficient (symbolized by the letter r ) is normally used to summarize the association, or correlation, between two variables. The correlation coefficient can range from −1 (indicating a very strong negative relationship between the variables) to +1 (indicating a very strong positive relationship between the variables). Research has found that there is a positive correlation between the use of violent video games and the incidence of aggressive behavior and that the size of the correlation is about r = .30 (Bushman & Huesmann, 2010).

One advantage of correlational research designs is that, like observational research (and in comparison with experimental research designs in which the researcher frequently creates relatively artificial situations in a laboratory setting), they are often used to study people doing the things that they do every day. And correlational research designs also have the advantage of allowing prediction. When two or more variables are correlated, we can use our knowledge of a person’s score on one of the variables to predict his or her likely score on another variable. Because high-school grade point averages are correlated with college grade point averages, if we know a person’s high-school grade point average, we can predict his or her likely college grade point average. Similarly, if we know how many violent video games a child plays, we can predict how aggressively he or she will behave. These predictions will not be perfect, but they will allow us to make a better guess than we would have been able to if we had not known the person’s score on the first variable ahead of time.

Despite their advantages, correlational designs have a very important limitation. This limitation is that they cannot be used to draw conclusions about the causal relationships among the variables that have been measured. An observed correlation between two variables does not necessarily indicate that either one of the variables caused the other. Although many studies have found a correlation between the number of violent video games that people play and the amount of aggressive behaviors they engage in, this does not mean that viewing the video games necessarily caused the aggression. Although one possibility is that playing violent games increases aggression,

another possibility is that the causal direction is exactly opposite to what has been hypothesized. Perhaps increased aggressiveness causes more interest in, and thus increased viewing of, violent games. Although this causal relationship might not seem as logical to you, there is no way to rule out the possibility of such reverse causation on the basis of the observed correlation.

Still another possible explanation for the observed correlation is that it has been produced by the presence of another variable that was not measured in the research. Common-causal variables (also known as third variables) are variables that are not part of the research hypothesis but that cause both the predictor and the outcome variable and thus produce the observed correlation between them ( Figure 1.6 “Correlation and Causality” ). It has been observed that students who sit in the front of a large class get better grades than those who sit in the back of the class. Although this could be because sitting in the front causes the student to take better notes or to understand the material better, the relationship could also be due to a common-causal variable, such as the interest or motivation of the students to do well in the class. Because a student’s interest in the class leads him or her to both get better grades and sit nearer to the teacher, seating position and class grade are correlated, even though neither one caused the other.

Figure 1.6 Correlation and Causality

The correlation between where we sit in a large class and our grade in the class is likely caused by the influence of one or more common-causal variables.

The possibility of common-causal variables must always be taken into account when considering correlational research designs. For instance, in a study that finds a correlation between playing violent video games and aggression, it is possible that a common-causal variable is producing the relationship. Some possibilities include the family background, diet, and hormone levels of the children. Any or all of these potential common-causal variables might be creating the observed correlation between playing violent video games and aggression. Higher levels of the male sex hormone testosterone, for instance, may cause children to both watch more violent TV and behave more aggressively.

I like to think of common-causal variables in correlational research designs as “mystery” variables, since their presence and identity is usually unknown to the researcher because they have not been measured. Because it is not possible to measure every variable that could possibly cause both variables, it is always possible that there is an unknown common-causal variable. For this reason, we are left with the basic limitation of correlational research: Correlation does not imply causation.

Experimental Research

The goal of much research in social psychology is to understand the causal relationships among variables, and for this we use experiments. Experimental research designs are research designs that include the manipulation of a given situation or experience for two or more groups of individuals who are initially created to be equivalent, followed by a measurement of the effect of that experience .

In an experimental research design, the variables of interest are called the independent variables and the dependent variables. The independent variable refers to the situation that is created by the experimenter through the experimental manipulations , and the dependent variable refers to the variable that is measured after the manipulations have occurred . In an experimental research design, the research hypothesis is that the manipulated independent variable (or variables) causes changes in the measured dependent variable (or variables). We can diagram the prediction like this, using an arrow that points in one direction to demonstrate the expected direction of causality:

viewing violence (independent variable) → aggressive behavior (dependent variable)

Consider an experiment conducted by Anderson and Dill (2000), which was designed to directly test the hypothesis that viewing violent video games would cause increased aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played the video game that they had been given for 15 minutes. Then, after the play, they participated in a competitive task with another student in which they had a chance to deliver blasts of white noise through the earphones of their opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design and the results of the experiment are shown in Figure 1.7 “An Experimental Research Design (After Anderson & Dill, 2000)” .

Figure 1.7 An Experimental Research Design (After Anderson & Dill, 2000)

Two advantages of the experimental research design are (a) an assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable and (b) the creation of initial equivalence between the conditions of the experiment (in this case, by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to measuring the dependent variable. This eliminates the possibility of reverse causation. Second, the experimental manipulation allows ruling out the possibility of common-causal variables that cause both the independent variable and the dependent variable. In experimental designs, the influence of common-causal variables is controlled, and thus eliminated, by creating equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , which involves determining separately for each participant which condition he or she will experience through a random process, such as drawing numbers out of an envelope or using a website such as http://randomizer.org . Anderson and Dill first randomly assigned about 100 participants to each of their two groups. Let’s call them Group A and Group B. Because they used random assignment to conditions, they could be confident that before the experimental manipulation occurred , the students in Group A were, on average , equivalent to the students in Group B on every possible variable , including variables that are likely to be related to aggression, such as family, peers, hormone levels, and diet—and, in fact, everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent video game and the participants in Group B the nonviolent video game. Then they compared the dependent variable (the white noise blasts) between the two groups and found that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game. Because they had created initial equivalence between the groups, when the researchers observed differences in the duration of white noise blasts between the two groups after the experimental manipulation, they could draw the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was which video game they had played.

When we create a situation in which the groups of participants are expected to be equivalent before the experiment begins, when we manipulate the independent variable before we measure the dependent variable, and when we change only the nature of independent variables between the conditions, then we can be confident that it is the independent variable that caused the differences in the dependent variable. Such experiments are said to have high internal validity , where internal validity refers to the confidence with which we can draw conclusions about the causal relationship between the variables .

Despite the advantage of determining causation, experimental research designs do have limitations. One is that the experiments are usually conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. To counter this, in some cases experiments are conducted in everyday settings—for instance, in schools or other organizations . Such field experiments are difficult to conduct because they require a means of creating random assignment to conditions, and this is frequently not possible in natural settings.

A second and perhaps more important limitation of experimental research designs is that some of the most interesting and important social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join suicide cults, these relationships must be assessed using correlational designs because it is simply not possible to manipulate mob size or cult membership.

Factorial Research Designs

Social psychological experiments are frequently designed to simultaneously study the effects of more than one independent variable on a dependent variable. Factorial research designs are experimental designs that have two or more independent variables . By using a factorial design, the scientist can study the influence of each variable on the dependent variable (known as the main effects of the variables) as well as how the variables work together to influence the dependent variable (known as the interaction between the variables). Factorial designs sometimes demonstrate the person by situation interaction.

In one such study, Brian Meier and his colleagues (Meier, Robinson, & Wilkowski, 2006) tested the hypothesis that exposure to aggression-related words would increase aggressive responses toward others. Although they did not directly manipulate the social context, they used a technique common in social psychology in which they primed (i.e., activated) thoughts relating to social settings. In their research, half of their participants were randomly assigned to see words relating to aggression and the other half were assigned to view neutral words that did not relate to aggression. The participants in the study also completed a measure of individual differences in agreeableness —a personality variable that assesses the extent to which the person sees themselves as compassionate, cooperative, and high on other-concern.

Then the research participants completed a task in which they thought they were competing with another student. Participants were told that they should press the space bar on the computer as soon as they heard a tone over their headphones, and the person who pressed the button the fastest would be the winner of the trial. Before the first trial, participants set the intensity of a blast of white noise that would be delivered to the loser of the trial. The participants could choose an intensity ranging from 0 (no noise) to the most aggressive response (10, or 105 decibels). In essence, participants controlled a “weapon” that could be used to blast the opponent with aversive noise, and this setting became the dependent variable. At this point, the experiment ended.

Figure 1.8 A Person-Situation Interaction

In this experiment by Meier, Robinson, and Wilkowski (2006) the independent variables are type of priming (aggression or neutral) and participant agreeableness (high or low). The dependent variable is the white noise level selected (a measure of aggression). The participants who were low in agreeableness became significantly more aggressive after seeing aggressive words, but those high in agreeableness did not.

As you can see in Figure 1.8 “A Person-Situation Interaction” , there was a person by situation interaction. Priming with aggression-related words (the situational variable) increased the noise levels selected by participants who were low on agreeableness, but priming did not increase aggression (in fact, it decreased it a bit) for students who were high on agreeableness. In this study, the social situation was important in creating aggression, but it had different effects for different people.

Deception in Social Psychology Experiments

You may have wondered whether the participants in the video game study and that we just discussed were told about the research hypothesis ahead of time. In fact, these experiments both used a cover story — a false statement of what the research was really about . The students in the video game study were not told that the study was about the effects of violent video games on aggression, but rather that it was an investigation of how people learn and develop skills at motor tasks like video games and how these skills affect other tasks, such as competitive games. The participants in the task performance study were not told that the research was about task performance . In some experiments, the researcher also makes use of an experimental confederate — a person who is actually part of the experimental team but who pretends to be another participant in the study . The confederate helps create the right “feel” of the study, making the cover story seem more real.

In many cases, it is not possible in social psychology experiments to tell the research participants about the real hypotheses in the study, and so cover stories or other types of deception may be used. You can imagine, for instance, that if a researcher wanted to study racial prejudice, he or she could not simply tell the participants that this was the topic of the research because people may not want to admit that they are prejudiced, even if they really are. Although the participants are always told—through the process of informed consent —as much as is possible about the study before the study begins, they may nevertheless sometimes be deceived to some extent. At the end of every research project, however, participants should always receive a complete debriefing in which all relevant information is given, including the real hypothesis, the nature of any deception used, and how the data are going to be used.

Interpreting Research

No matter how carefully it is conducted or what type of design is used, all research has limitations. Any given research project is conducted in only one setting and assesses only one or a few dependent variables. And any one study uses only one set of research participants. Social psychology research is sometimes criticized because it frequently uses college students from Western cultures as participants (Henrich, Heine, & Norenzayan, 2010). But relationships between variables are only really important if they can be expected to be found again when tested using other research designs, other operational definitions of the variables, other participants, and other experimenters, and in other times and settings.

External validity refers to the extent to which relationships can be expected to hold up when they are tested again in different ways and for different people . Science relies primarily upon replication —that is, the repeating of research —to study the external validity of research findings. Sometimes the original research is replicated exactly, but more often, replications involve using new operational definitions of the independent or dependent variables, or designs in which new conditions or variables are added to the original design. And to test whether a finding is limited to the particular participants used in a given research project, scientists may test the same hypotheses using people from different ages, backgrounds, or cultures. Replication allows scientists to test the external validity as well as the limitations of research findings.

In some cases, researchers may test their hypotheses, not by conducting their own study, but rather by looking at the results of many existing studies, using a meta-analysis — a statistical procedure in which the results of existing studies are combined to determine what conclusions can be drawn on the basis of all the studies considered together . For instance, in one meta-analysis, Anderson and Bushman (2001) found that across all the studies they could locate that included both children and adults, college students and people who were not in college, and people from a variety of different cultures, there was a clear positive correlation (about r = .30) between playing violent video games and acting aggressively. The summary information gained through a meta-analysis allows researchers to draw even clearer conclusions about the external validity of a research finding.

Figure 1.9 Some Important Aspects of the Scientific Approach

It is important to realize that the understanding of social behavior that we gain by conducting research is a slow, gradual, and cumulative process. The research findings of one scientist or one experiment do not stand alone—no one study “proves” a theory or a research hypothesis. Rather, research is designed to build on, add to, and expand the existing research that has been conducted by other scientists. That is why whenever a scientist decides to conduct research, he or she first reads journal articles and book chapters describing existing research in the domain and then designs his or her research on the basis of the prior findings. The result of this cumulative process is that over time, research findings are used to create a systematic set of knowledge about social psychology ( Figure 1.9 “Some Important Aspects of the Scientific Approach” ).

Key Takeaways

• Social psychologists study social behavior using an empirical approach. This allows them to discover results that could not have been reliably predicted ahead of time and that may violate our common sense and intuition.
• The variables that form the research hypothesis, known as conceptual variables, are assessed using measured variables by using, for instance, self-report, behavioral, or neuroimaging measures.
• Observational research is research that involves making observations of behavior and recording those observations in an objective manner. In some cases, it may be the only approach to studying behavior.
• Correlational and experimental research designs are based on developing falsifiable research hypotheses.
• Correlational research designs allow prediction but cannot be used to make statements about causality. Experimental research designs in which the independent variable is manipulated can be used to make statements about causality.
• Social psychological experiments are frequently factorial research designs in which the effects of more than one independent variable on a dependent variable are studied.
• All research has limitations, which is why scientists attempt to replicate their results using different measures, populations, and settings and to summarize those results using meta-analyses.

Exercises and Critical Thinking

1. Find journal articles that report observational, correlational, and experimental research designs. Specify the research design, the research hypothesis, and the conceptual and measured variables in each design. 2.

Consider the following variables that might have contributed to teach of the following events. For each one, (a) propose a research hypothesis in which the variable serves as an independent variable and (b) propose a research hypothesis in which the variable serves as a dependent variable.

• Liking another person
• Life satisfaction

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Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Bushman, B. J., & Huesmann, L. R. (2010). Aggression. In S. T. Fiske, D. T. Gilbert, & G. Lindzey (Eds.), Handbook of social psychology (5th ed., Vol. 2, pp. 833–863). Hoboken, NJ: John Wiley & Sons.

Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302 (5643), 290–292.

Festinger, L., Riecken, H. W., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world . Minneapolis, MN: University of Minnesota Press.

Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science, 293 (5537), 2105–2108.

Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33 (2–3), 61–83.

Lieberman, M. D., Hariri, A., Jarcho, J. M., Eisenberger, N. I., & Bookheimer, S. Y. (2005). An fMRI investigation of race-related amygdala activity in African-American and Caucasian-American individuals. Nature Neuroscience, 8 (6), 720–722.

Lilienfeld, S. O. (2011, June 13). Public skepticism of psychology: Why many people perceive the study of human behavior as unscientific. American Psychologist. doi: 10.1037/a0023963

Meier, B. P., Robinson, M. D., & Wilkowski, B. M. (2006). Turning the other cheek: Agreeableness and the regulation of aggression-related crimes. Psychological Science, 17 (2), 136–142.

Morewedge, C. K., Gray, K., & Wegner, D. M. (2010). Perish the forethought: Premeditation engenders misperceptions of personal control. In R. R. Hassin, K. N. Ochsner, & Y. Trope (Eds.), Self-control in society, mind, and brain (pp. 260–278). New York, NY: Oxford University Press.

Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. E. (2002). Rethinking feelings: An fMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14 (8), 1215–1229.

Preston, J., & Wegner, D. M. (2007). The eureka error: Inadvertent plagiarism by misattributions of effort. Journal of Personality and Social Psychology, 92 (4), 575–584.

Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland, C. L., Trawalter, S., Richeson, J. A., Baird, A. A., Gordon, H. L., Heatherton, T. F., Wyland, C. L., Trawalter, S., et al.#8230.

Shelton, J. N. (2003). An fMRI investigation of the impact of interracial contact on executive function. Nature Neuroscience, 6 (12), 1323–1328.

Principles of Social Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Module 2: Research Methods in Social Psychology

Module Overview

In Module 2 we will address the fact that psychology is the scientific study of behavior and mental processes. We will do this by examining the steps of the scientific method and describing the five major designs used in psychological research. We will also differentiate between reliability and validity and their importance for measurement. Psychology has very clear ethical standards and procedures for scientific research. We will discuss these but also why they are needed. Finally, psychology as a field, but especially social psychology as a subfield, is faced with a replication crisis and issues with the generalizability of its findings. These will be explained to close out the module.

Module Outline

2.1. The Scientific Method

2.2. research designs used by social psychologists, 2.3. reliability and validity, 2.4. research ethics, 2.5. issues in social psychology.

Module Learning Outcomes

• Clarify what it means for psychology to be scientific by examining the steps of the scientific method and the three cardinal features of science.
• Outline the five main research methods used in psychology and clarify how they are utilized in social psychology.
• Differentiate and explain the concepts of reliability and validity.
• Describe key features of research ethics.
• Clarify the nature of the replication crisis in psychology and the importance of generalizability.

Section Learning Objectives

• Define scientific method.
• Outline and describe the steps of the scientific method, defining all key terms.
• Identify and clarify the importance of the three cardinal features of science.

In Module 1, we learned that psychology was the scientific study of behavior and mental processes. We will spend quite a lot of time on the behavior and mental processes part, but before we proceed, it is prudent to elaborate more on what makes psychology scientific. In fact, it is safe to say that most people not within our discipline or a sister science, would be surprised to learn that psychology utilizes the scientific method at all.

So what is the scientific method? Simply, the scientific method is a systematic method for gathering knowledge about the world around us. The key word here is that it is systematic meaning there is a set way to use it. What is that way? Well, depending on what source you look at it can include a varying number of steps. For our purposes, the following will be used:

Table 2.1: The Steps of the Scientific Method

Science has at its root three cardinal features that we will see play out time and time again throughout this book, and as mentioned in Module 1. They are:

• Observation – In order to know about the world around us we must be able to see it firsthand. In relation to social psychology, we know our friend and his parents pretty well, and so in our time with them have observed the influence they exert on his life.
• Experimentation – To be able to make causal or cause and effect statements, we must be able to isolate variables. We have to manipulate one variable and see the effect of doing so on another variable. Experimentation is the primary method social psychology uses to test its hypotheses.
• Measurement – How do we know whether or not our friend is truly securely attached to his parents? Well, simply we measure attachment. In order to do that, we could give our friend a short questionnaire asking about his attachment pattern to his parents. For this questionnaire, let’s say we use a 5-point scale for all questions (with 1 meaning the question does not apply to 5 meaning it definitely is true or matters). If there were 10 questions, then our friend would have a score between 10 and 50. The 10 would come from him answering every question with a 1 and the 50 from answering every question with a 5. If you are not aware, there are four main styles of attachment (secure, anxious-ambivalent, avoidant, and disorganized-disoriented). We would have 2-3 questions assessing each of the 4 styles meaning that if we had 2 questions for that style, the score would range from 2 to 10. If 3 questions, the range would be 3 to 15. The higher the score, the more likely the person exhibits that style to the parent and our friend should only have a high score on one of the four styles if our scale correctly assesses attachment. We will discuss reliability and validity in Section 2.3.
• List the five main research methods used in psychology.
• Describe observational research, listing its advantages and disadvantages.
• Describe case study research, listing its advantages and disadvantages.
• Describe survey research, listing its advantages and disadvantages.
• Describe correlational research, listing its advantages and disadvantages.
• Describe experimental research, listing its advantages and disadvantages.
• State the utility and need for multimethod research.

Step 3 called on the scientist to test their hypothesis. Psychology as a discipline uses five main research designs. These include observational research, case studies, surveys, correlational designs, and experiments.

2.2.1. Observational Research

In terms of naturalistic observation , the scientist studies human or animal behavior in its natural environment which could include the home, school, or a forest. The researcher counts, measures, and rates behavior in a systematic way and at times uses multiple judges to ensure accuracy in how the behavior is being measured. This is called inter-rater reliability as you will see in Section 2.3. The advantage of this method is that you witness behavior as it occurs and it is not tainted by the experimenter. The disadvantage is that it could take a long time for the behavior to occur and if the researcher is detected then this may influence the behavior of those being observed. In the case of the latter, the behavior of the observed becomes artificial .

Laboratory observation involves observing people or animals in a laboratory setting. The researcher might want to know more about parent-child interactions and so brings a mother and her child into the lab to engage in preplanned tasks such as playing with toys, eating a meal, or the mother leaving the room for a short period of time. The advantage of this method over the naturalistic method is that the experimenter can use sophisticated equipment and videotape the session to examine it at a later time. The problem is that since the subjects know the experimenter is watching them, their behavior could become artificial from the start.

2.2.1.1. Example of an observational social psychology study. Griffiths (1991) studied the gambling behavior of adolescents by observing the clientele of 33 arcades in the UK. He used participant (when the researcher becomes an active participant in the group they are studying) and non-participant observation methodologies and found that adolescent gambling depended on the time of day and the time of year, and regular players had stereotypical behaviors and conformed to specific rules of etiquette. They played for fun, to win, to socialize, for excitement, and/or to escape.

2.2.2. Case Studies

Psychology can also utilize a detailed description of one person or a small group based on careful observation. This was the approach the founder of psychoanalysis, Sigmund Freud, took to develop his theories. The advantage of this method is that you arrive at a rich description of the behavior being investigated but the disadvantage is that what you are learning may be unrepresentative of the larger population and so lacks generalizability . Again, bear in mind that you are studying one person or a very small group. Can you possibly make conclusions about all people from just one or even five or ten? The other issue is that the case study is subject to the bias of the researcher in terms of what is included in the final write up and what is left out. Despite these limitations, case studies can lead us to novel ideas about the cause of behavior and help us to study unusual conditions that occur too infrequently to study with large sample sizes and in a systematic way. Though our field does make use of the case study methodology, social psychology does not frequently use the design.

2.2.2.1. Example of a case study from clinical psychology. In 1895, the book, Studies on Hysteria , was published by Josef Breuer (1842-1925) and Sigmund Freud (1856-1939), and marked the birth of psychoanalysis, though Freud did not use this actual term until a year later. The book published several case studies, including that of Anna O., born February 27, 1859 in Vienna to Jewish parents Siegmund and Recha Pappenheim, strict Orthodox adherents and considered millionaires at the time. Bertha, known in published case studies as Anna O., was expected to complete the formal education of a girl in the upper middle class which included foreign language, religion, horseback riding, needlepoint, and piano. She felt confined and suffocated in this life and took to a fantasy world she called her “private theater.” Anna also developed hysteria to include symptoms such as memory loss, paralysis, disturbed eye movements, reduced speech, nausea, and mental deterioration. Her symptoms appeared as she cared for her dying father and her mother called on Breuer to diagnose her condition (note that Freud never actually treated her). Hypnosis was used at first and relieved her symptoms. Breuer made daily visits and allowed her to share stories from her private theater which he came to call “talking cure” or “chimney sweeping.” Many of the stories she shared were actually thoughts or events she found troubling and reliving them helped to relieve or eliminate the symptoms. Breuer’s wife, Mathilde, became jealous of her husband’s relationship with the young girl, leading Breuer to terminate treatment in the June of 1882 before Anna had fully recovered. She relapsed and was admitted to Bellevue Sanatorium on July 1, eventually being released in October of the same year. With time, Anna O. did recover from her hysteria and went on to become a prominent member of the Jewish Community, involving herself in social work, volunteering at soup kitchens, and becoming ‘House Mother’ at an orphanage for Jewish girls in 1895. Bertha (Anna O.) became involved in the German Feminist movement, and in 1904 founded the League of Jewish Women. She published many short stories; a play called Women’s Rights , in which she criticized the economic and sexual exploitation of women, and wrote a book in 1900 called The Jewish Problem in Galicia , in which she blamed the poverty of the Jews of Eastern Europe on their lack of education. In 1935 she was diagnosed with a tumor and was summoned by the Gestapo in 1936 to explain anti-Hitler statements she had allegedly made. She died shortly after this interrogation on May 28, 1936. Freud considered the talking cure of Anna O. to be the origin of psychoanalytic therapy and what would come to be called the cathartic method.

To learn more about observational and case study designs, please take a look at our Research Methods in Psychology textbook by visiting:

https://opentext.wsu.edu/carriecuttler/chapter/observational-research/

For more on Anna O., please see:

https://www.psychologytoday.com/blog/freuds-patients-serial/201201/bertha-pappenheim-1859-1936

2.2.3. Surveys/Self-Report Data

A survey is a questionnaire consisting of at least one scale with some number of questions which assess a psychological construct of interest such as parenting style, depression, locus of control, attitudes, or sensation seeking behavior. It may be administered by paper and pencil or computer. Surveys allow for the collection of large amounts of data quickly but the actual survey could be tedious for the participant and social desirability , when a participant answers questions dishonestly so that he/she is seen in a more favorable light, could be an issue. For instance, if you are asking high school students about their sexual activity they may not give genuine answers for fear that their parents will find out. Or if you wanted to know about prejudicial attitudes of a group of people, you could use the survey method. You could alternatively gather this information via an interview in a structured or unstructured fashion. Important to survey research is that you have random sampling or when everyone in the population has an equal chance of being included in the sample. This helps the survey to be representative of the population and in terms of key demographic variables such as gender, age, ethnicity, race, education level, and religious orientation.

To learn more about the survey research design, please take a look at our Research Methods in Psychology textbook by visiting:

https://opentext.wsu.edu/carriecuttler/chapter/7-1-overview-of-survey-research/

2.2.4. Correlational Research

This research method examines the relationship between two variables or two groups of variables. A numerical measure of the strength of this relationship is derived, called the correlation coefficient , and can range from -1.00, a perfect inverse relationship meaning that as one variable goes up the other goes down, to 0 or no relationship at all, to +1.00 or a perfect relationship in which as one variable goes up or down so does the other. In terms of a negative correlation we might say that as a parent becomes more rigid, controlling, and cold, the attachment of the child to the parent goes down. In contrast, as a parent becomes warmer, more loving, and provides structure, the child becomes more attached. The advantage of correlational research is that you can correlate anything. The disadvantage is that you can correlate anything. Variables that really do not have any relationship to one another could be viewed as related. Yes. This is both an advantage and a disadvantage. For instance, we might correlate instances of making peanut butter and jelly sandwiches with someone we are attracted to sitting near us at lunch. Are the two related? Not likely, unless you make a really good PB&J but then the person is probably only interested in you for food and not companionship. The main issue here is that correlation does not allow you to make a causal statement.

To learn more about the correlational research design, please take a look at our Research Methods in Psychology textbook by visiting:

https://opentext.wsu.edu/carriecuttler/chapter/correlational-research/

2.2.5. Example of a Study Using Survey and Correlational Designs

Roccas, Sagiv, Schwartz, and Knafo (2002) examined the relationship of the big five personality traits and values by administering the Schwartz (1992) Values survey, NEO-PI, a positive affect scale, and a single item assessing religiosity to introductory to psychology students at an Israeli university. For Extraversion, it was found that values that define activity, challenge, excitement, and pleasure as desirable goals in life (i.e. stimulation, hedonism, and achievement) were important while valuing self-denial or self-abnegation, expressed in traditional values, was antithetical.

For Openness, values that emphasize intellectual and emotional autonomy, acceptance and cultivation of diversity, and pursuit of novelty and change (i.e. universalism, self-direction, and stimulation) were important while conformity, security, and tradition values were incompatible. Benevolence, tradition, and to a lesser degree conformity, were important for Agreeableness while power and achievement correlated negatively. In terms of Conscientiousness (C), there was a positive correlation with security values as both share the goal of maintaining smooth interpersonal relations and avoiding disruption of social order and there was a negative correlation with stimulation, indicating an avoidance of risk as a motivator of C.

Finally, there was little association of values with the domain of Neuroticism but a closer inspection of the pattern of correlations with the facets of N suggests two components. First, the angry hostility and impulsiveness facets could be called extrapunitive since the negative emotion is directed outward and tends to correlate positively with hedonism and stimulation values and negatively with benevolence, tradition, conformity, and C values. Second, the anxiety, depression, self-consciousness, and vulnerability facets could be called intrapunitive since the negative emotion is directed inward. This component tends to correlate positively with tradition values and negatively with achievement and stimulation values.

2.2.6. Experiments

An experiment is a controlled test of a hypothesis in which a researcher manipulates one variable and measures its effect on another variable. The variable that is manipulated is called the independent variable (IV) and the one that is measured is called the dependent variable (DV) . A common feature of experiments is to have a control group that does not receive the treatment or is not manipulated and an experimental group that does receive the treatment or manipulation. If the experiment includes random assignment participants have an equal chance of being placed in the control or experimental group. The control group allows the researcher to make a comparison to the experimental group, making a causal statement possible, and stronger.

2.2.6.1. Example of an experiment.    Allison and Messick (1990) led subjects to believe they were the first of six group members to take points from a common resource pool and that they could take as many points as desired which could later be exchanged for cash. Three variables were experimentally manipulated. First, subjects in the low payoff condition were led to believe the pool was only 18 or 21 points in size whereas those in the high payoff condition were told the pool consisted of either 24 or 27 points. Second, the pools were divisible (18 and 24) or nondivisible (21 or 27). Third, half of the subjects were placed in the fate control condition and told that if the requests from the six group members exceeded the pool size, then no one could keep any points, while the other half were in the no fate control condition and told there would be no penalties for overconsumption of the pool.  Finally, data for a fourth variable, social values, was collected via questionnaire four weeks prior to participation. In all, the study employed a 2 (fate control) x 2 (payoff size) x 2 (divisibility) x 2 (social values) between-subjects factorial design.

Results showed that subjects took the least number of points from the resource pool when the resource was divisible, the payoffs were low, and there was no fate control. On the other hand, subjects took the most points when the resource was nondivisible, the payoffs were high, and subjects were noncooperative. To further demonstrate this point, Allison and Messick (1990) counted the number of inducements to which participants were exposed. This number ranged from 0 to 4 inducements. Subjects took between one-fifth and one-fourth when there were one or two inducements, took about one-third when there were three inducements, and about half of the pool when all four were present. They state that an equal division rule was used when there were no temptations to violate equality but as the number of temptations increased, subjects became progressively more likely to overconsume the pool. The authors conclude that the presence of competing cues/factors tends to invite the use of self-serving rules to include “First-come, first-served” and “People who get to go first take more.”

To learn more about the experimental research design, please take a look at our Research Methods in Psychology textbook by visiting:

https://opentext.wsu.edu/carriecuttler/chapter/experiment-basics/

2.2.7. Multi-Method Research

As you have seen above, no single method alone is perfect. All have their strengths and limitations. As such, for the psychologist to provide the clearest picture of what is affecting behavior or mental processes, several of these approaches are typically employed at different stages of the research process. This is called multi-method research.

2.2.8. Archival Research

Another technique used by psychologists is called archival research or when the researcher analyzes data that has already been collected and for another purpose. For instance, a researcher may request data from high schools about a student’s GPA and their SAT and/or ACT score(s) and then obtain their four-year GPA from the university they attended. This can be used to make a prediction about success in college and which measure – GPA or standardized test score – is the better predictor.

2.2.9. Meta-Analysis

Meta-analysis is a statistical procedure that allows a researcher to combine data from more than one study. For example, Shariff et al. (2015) published an article on religious priming and prosociality in Personality and Social Psychology Review . The authors used effect-size analyses, p- curve analyses, and adjustments for publication bias (no worries, you don’t have to understand any of that), to evaluate the robustness of four types of religious priming, how religion affects prosocial behavior, and whether religious-priming effects generalize to those who are loosely or not religious at all. Results were presented across 93 studies and 11,653 participants and showed that religious priming has robust effects in relation to a variety of outcome measures, prosocial behavior included. It did not affect non-religious people though.

2.2.10. Communicating Results

In scientific research, it is common practice to communicate the findings of our investigation. By reporting what we found in our study other researchers can critique our methodology and address our limitations. Publishing allows psychology to grow its knowledge base about human behavior. We can also see where gaps still exist. We move it into the public domain so others can read and comment on it. Scientists can also replicate what we did and possibly extend our work if it is published.

There are several ways to communicate our findings. We can do so at conferences in the form of posters or oral presentations, through newsletters from APA itself or one of its many divisions or other organizations, or through research journals and specifically scientific research articles. Published journal articles represent a form of communication between scientists and in them, the researchers describe how their work relates to previous research, how it replicates and/or extends this work, and what their work might mean theoretically.

Research articles begin with an abstract or a 150-250 word summary of the entire article. The purpose is to describe the experiment and allows the reader to make a decision about whether he or she wants to read it further. The abstract provides a statement of purpose, overview of the methods, main results, and a brief statement of the conclusion. Keywords are also given that allow for students and other researchers alike to find the article when doing a search.

The abstract is followed by four major sections as described:

• Introduction – The first section is designed to provide a summary of the current literature as it relates to your topic. It helps the reader to see how you arrived at your hypothesis and the design of your study. Essentially, it gives the logic behind the decisions you made. You also state the purpose and share your predictions or hypothesis.
• Method – Since replication is a required element of science, we must have a way to share information on our design and sample with readers. This is the essence of the method section and covers three major aspects of your study – your participants, materials or apparatus, and procedure. The reader needs to know who was in your study so that limitations related to generalizability of your findings can be identified and investigated in the future. You will also state your operational definition, describe any groups you used, random sampling or assignment procedures, information about how a scale was scored, etc. Think of the Method section as a cookbook. The participants are your ingredients, the materials or apparatus are whatever tools you will need, and the procedure is the instructions for how to bake the cake.
• Results – In this section you state the outcome of your experiment and whether they were statistically significant or not. You can also present tables and figures.
• Discussion – In this section you start by restating the main findings and hypothesis of the study. Next, you offer an interpretation of the findings and what their significance might be. Finally, you state strengths and limitations of the study which will allow you to propose future directions.

Whether you are writing a research paper for a class or preparing an article for publication, or reading a research article, the structure and function of a research article is the same. Understanding this will help you when reading social psychological articles.

• Clarify why reliability and validity are important.
• Define reliability and list and describe forms it takes.
• Define validity and list and describe forms it takes.

Recall that measurement involves the assignment of scores to an individual which are used to represent aspects of the individual such as how conscientious they are or their level of depression. Whether or not the scores actually represent the individual is what is in question. Cuttler (2017) says in her book Research Methods in Psychology, “Psychologists do not simply  assume  that their measures work. Instead, they collect data to demonstrate  that they work. If their research does not demonstrate that a measure works, they stop using it.” So how do they demonstrate that a measure works? This is where reliability and validity come in.

2.3.1. Reliability

First, reliability describes how consistent a measure is. It can be measured in terms of test-retest reliability , or how reliable the measure is across time, internal consistency , or the “consistency of people’s responses across the items on multiple-item measures,” (Cuttler, 2017), and finally inter-rater reliability , or how consistent different observers are when making judgments. In terms of inter-rater reliability, Cuttler (2017) writes, “Inter-rater reliability would also have been measured in Bandura’s Bobo doll study. In this case, the observers’ ratings of how many acts of aggression a particular child committed while playing with the Bobo doll should have been highly positively correlated.”

2.3.2. Validity

A measure is considered to be valid if its scores represent the variable it is said to measure. For instance, if a scale says it measures depression, and it does, then we can say it is valid. Validity can take many forms. First, face validity is “the extent to which a measurement method appears “on its face” to measure the construct of interest” (Cuttler, 2017). A scale purported to measure values should have questions about values such as benevolence, conformity, and self-direction, and not questions about depression or attitudes toward toilet paper.

Content validity is to what degree a measure covers the construct of interest. Cuttler (2017) says, “… consider that attitudes are usually defined as involving thoughts, feelings, and actions toward something. By this conceptual definition, a person has a positive attitude toward exercise to the extent that he or she thinks positive thoughts about exercising, feels good about exercising, and actually exercises.”

Oftentimes, we expect a person’s scores on one measure to be correlated with scores on another measure that we expect it to be related to, called criterion validity . For instance, consider parenting style and attachment. We would expect that if a person indicates on one scale that their father was authoritarian (or dictatorial) then attachment would be low or insecure. In contrast, if the mother was authoritative (or democratic) we would expect the child to show a secure attachment style.

As researchers we expect that our results will generalize from our sample to the larger population. This was the issue with case studies as the sample is too small to make conclusions about everyone. If our results do generalize from the circumstances under which our study was conducted to similar situations, then we can say our study has external validity . External validity is also affected by how real the research is. Two types of realism are possible. First, mundane realism occurs when the research setting closely resembles the real world setting. Experimental realism is the degree to which the experimental procedures that are used feel real to the participant. It does not matter if they really mirror real life but that they only appear real to the participant. If so, his or her behavior will be more natural and less artificial.

In contrast, a study is said to have good internal validity when we can confidently say that the effect on the dependent variable (the one that is measured) was due solely to our manipulation or the independent variable. A confound occurs when a factor other than the independent variable leads to changes in the dependent variable.

To learn more about reliability and validity, please visit: https://opentext.wsu.edu/carriecuttler/chapter/reliability-and-validity-of-measurement/

• Exemplify instances of ethical misconduct in research.
• List and describe principles of research ethics.

Throughout this module so far, we have seen that it is important for researchers to understand the methods they are using. Equally important, they must understand and appreciate ethical standards in research. The American Psychological Association identifies high standards of ethics and conduct as one of its four main guiding principles or missions. To read about the other three, please visit https://www.apa.org/about/index.aspx . So why are ethical standards needed and what do they look like?

2.4.1. Milgram’s Study on Learning…or Not

Possibly, the one social psychologist students know about the most is Stanley Milgram, if not by name, then by his study on obedience using shock (Milgram, 1974). Essentially, two individuals came to each experimental session but only one of these two individuals was a participant. The other was what is called a confederate and is part of the study without the participant knowing. The confederate was asked to pick heads or tails and then a coin was flipped. As you might expect, the confederate always won and chose to be the learner . The “experimenter,” who was also a confederate, took him into one room where he was hooked up to wires and electrodes. This was done while the “teacher,” the actual participant, watched and added to the realism of what was being done. The teacher was then taken into an adjacent room where he was seated in front of a shock generator. The teacher was told it was his task to read a series of word pairs to the learner. Upon completion of reading the list, he would ask the learner one of the two words and it was the learner’s task to state what the other word in the pair was. If the learner incorrectly paired any of the words, he would be shocked. The shock generator started at 30 volts and increased in 15-volt increments up to 450 volts. The switches were labeled with terms such as “Slight shock,” “Moderate shock,” “Danger: Severe Shock,” and the final two switches were ominously labeled “XXX.”

As the experiment progressed, the teacher would hear the learner scream, holler, plead to be released, complain about a heart condition, or say nothing at all. When the learner stopped replying, the teacher would turn to the experimenter and ask what to do, to which the experimenter indicated for him to treat nonresponses as incorrect and shock the learner. Most participants asked the experimenter whether they should continue at various points in the experiment. The experimenter issued a series of commands to include, “Please continue,” “It is absolutely essential that you continue,” and “You have no other choice, you must go on.”

Any guesses as to what happened? What percent of the participants would you hypothesize actually shocked the learner to death? Milgram found that 65 percent of participants/teachers shocked the learner to the XXX switches which would have killed him. Why? They were told to do so. How do you think the participant felt when they realized that they could kill someone simply because they were told to do so?

Source: Milgram, S. (1974). Obedience to authority. New York, NY: Harper Perennial.

2.4.2. GO TO JAIL:  Go Directly to Jail. Do Not Pass Go. Do Not Collect $200

Early in the morning on Sunday, August 14, 1971, a Palo Alto, CA police car began arresting college students for committing armed robbery and burglary. Each suspect was arrested at his home, charged, read his Miranda rights, searched, handcuffed, and placed in the back of the police car as neighbors watched. At the station, the suspect was booked, read his rights again, and identified. He was then placed in a cell. How were these individuals chosen? Of course, they did not really commit the crimes they were charged with. The suspects had answered a newspaper ad requesting volunteers for a study of the psychological effects of prison life.

After screening individuals who applied to partake in the study, a final group of 24 were selected. These individuals did not have any psychological problems, criminal record, history of drug use, or mental disorder. They were paid $15 for their participation. The participants were divided into two groups through a flip of a coin. One half became the prison guards and the other half the prisoners. The prison was constructed by boarding up each end of a corridor in the basement of Stanford University’s Psychology building. This space was called “The Yard” and was the only place where the prisoners were permitted to walk, exercise, and eat. Prison cells were created by removing doors from some of the labs and replacing them with specially made doors with steel bars and cell numbers. A small closet was used for solitary confinement and was called “The Hole.” There were no clocks or windows in the prison and an intercom was used to make announcements to all prisoners. The suspects who were arrested were transported to “Stanford County Jail” to be processed. It was there they were greeted by the warden and told what the seriousness of their crime was. They were stripped searched and deloused, and the process was made to be intentionally degrading and humiliating. They were given uniforms with a prison ID number on it. This number became the only way they were referred to during their time. A heavy chain was placed on each prisoner’s right ankle which served the purpose of reminding them of how oppressive their environment was.

The guards were given no training and could do what they felt was necessary to maintain order and command the respect of the prisoners. They made their own set of rules and were supervised by the warden, who was played by another student at Stanford. Guards were dressed in identical uniforms, carried a whistle, held a billy club, and wore special mirror sun-glasses so no one could see their eyes or read their emotions. Three guards were assigned to each of the three hour shifts and supervised the nine prisoners. At 2:30 am they would wake the prisoners to take counts. This provided an opportunity to exert control and to get a feel for their role. Similarly, prisoners had to figure out how they were to act and at first, tried to maintain their independence. As you might expect, this led to confrontations between the prisoners and the guards resulting in the guards physically punishing the prisoners with push-ups.

The first day was relatively quiet, but on the second day, a rebellion broke out in which prisoners removed their caps, ripped off their numbers, and put their beds against their cell doors creating a barricade. The guards responded by obtaining a fire extinguisher and shooting a stream of the cold carbon dioxide solution at the prisoners. The cells were then broken into, the prisoners stripped, beds removed, ringleaders put into solitary confinement, and a program of harassment and intimidation of the remaining inmates began. Since 9 guards could not be on duty at all times to maintain order, a special “privilege cell” was established and the three prisoners least involved in the rebellion were allowed to stay in it. They were given their beds and uniforms back, could brush their teeth and take a bath, and were allowed to eat special food in the presence of the other six prisoners. This broke the solidarity among the prisoners.

Less than 36 hours after the study began a prisoner began showing signs of uncontrollable crying, acute emotional disturbance, rage, and disorganized thinking. Though his emotional problems were initially seen as an attempt to gain release which resulted in his being returned to the prison and used as an informant, the symptoms worsened and he had to be released from the study. Then there was the rumor of a mass escape by the prisoners which the guards worked to foil. When it was revealed that the prisoners were never actually going to attempt the prison break, the guards became very frustrated and made the prisoners engage in menial work, pushups, jumping jacks, and anything else humiliating that they could think of.

A Catholic priest was invited to evaluate how realistic the prison was. Each prisoner was interviewed individually and most introduced himself to the priest by his prison number and not his name. He offered to help them obtain a lawyer and some accepted. One prisoner was feeling ill (#819) and did not meet with the priest right away. When he did, he broke down and began to cry. He was quickly taken to another room and all prison garments taken off. While this occurred, the guards lined up the other prisoners and broke them out into a chant of “Prisoner #819 is a bad prisoner. Because of what Prisoner #819 did, my cell is a mess. Mr. Correctional Officer.” This further upset the prisoner and he was encouraged to leave, though he refused each time. He finally did agree to leave after the researcher (i.e. Zimbardo) told him what he was undergoing was just a research study and not really prison. The next day parole hearings were held and prisoners who felt they deserved to be paroled were interviewed one at a time. Most, when asked if they would give up the money they were making for their participation so they could leave, said yes.

In all, the study lasted just six days. Zimbardo noted that three types of guards emerged—tough but fair who followed the prison rules; “good guys” who never punished the prisoners and did them little favors; and finally those who were hostile, inventive in their employment of punishment, and who truly enjoyed the power they had. As for the prisoners, they coped with the events in the prison in different ways. Some fought back, others broke down emotionally, one developed a rash over his entire body, and some tried to be good prisoners and do all that the guards asked of them. No matter what strategy they used early on, by the end of the study they all were disintegrated as a group, and as individuals. The guards commanded blind obedience from all of the prisoners.

When asked later why he ended the study, Zimbardo cited two reasons. First, it became apparent that the guards were escalating their abuse of the prisoners in the middle of the night when they thought no one was watching. Second, Christina Maslach, a recent Stanford Ph.D. was asked to conduct interviews with the guards and prisoners and saw the prisoners being marched to the toilet with bags on their heads and legs chained together. She was outraged and questioned the study’s morality.

Source: http://www.prisonexp.org/

If you would like to learn more about the moral foundations of ethical research, please visit: https://opentext.wsu.edu/carriecuttler/chapter/moral-foundations-of-ethical-research/

2.4.3. Ethical Guidelines

Due to these studies, and others, the American Psychological Association (APA) established guiding principles for conducting psychological research. The principles can be broken down in terms of when they should occur during the process of a person participating in the study.

2.4.3.1. Before participating. First, researchers must obtain informed consent or when the person agrees to participate because they are told what will happen to them. They are given information about any risks they face, or potential harm that could come to them, whether physical or psychological. They are also told about confidentiality or the person’s right not to be identified. Since most research is conducted with students taking introductory psychology courses, they have to be given the right to do something other than a research study to likely earn required credits for the class. This is called an alternative activity and could take the form of reading and summarizing a research article. The amount of time taken to do this should not exceed the amount of time the student would be expected to participate in a study.

2.4.3.2. While participating. Participants are afforded the ability to withdraw or the person’s right to exit the study if any discomfort is experienced.

2.4.3.3. After participating . Once their participation is over, participants should be debriefed or when the true purpose of the study is revealed and they are told where to go if they need assistance and how to reach the researcher if they have questions. So can researchers deceive participants, or intentionally withhold the true purpose of the study from them? According to the APA, a minimal amount of deception is allowed.

Human research must be approved by an Institutional Review Board or IRB. It is the IRB that will determine whether the researcher is providing enough information for the participant to give consent that is truly informed, if debriefing is adequate, and if any deception is allowed or not.

If you would like to learn more about how to use ethics in your research, please read: https://opentext.wsu.edu/carriecuttler/chapter/putting-ethics-into-practice/

• Describe the replication crisis in psychology.
• Describe the issue with generalizability faced by social psychologists.

2.5.1. The Replication Crisis in Social Psychology

Today, the field of psychology faces what is called a replication crisis. Simply, published findings in psychology are not replicable, one of the hallmarks of science. Swiatkowski and Dompnier (2017) addressed this issue but with a focus on social psychology. They note that the field faces a confidence crisis due to events such as Diederick Staple intentionally fabricating data over a dozen years which lead to the retraction of over 50 published papers. They cite a study by John et al. (2012) in which 56% of 2,155 respondents admitted to collecting more data after discovering that the initial statistical test was not significant and 46% selectively reported studies that “worked” in a paper to be published. They also note that Nuijten et al. (2015) collected a sample of over 30,000 articles from the top 8 psychology journals and found that 1 in 8 possibly had an inconsistent p value that could have affected the conclusion the researchers drew.

So, how extensive is the issue? The Psychology Reproducibility Project was started to determine to what degree psychological effects from the literature could be replicated. One hundred published studies were attempted to be replicated by independent research teams and from different subfields in psychology. Only 39% of the findings were considered to be successfully replicated. For social psychology the results were worse. Only 25% were replicated.

Why might a study not replicate? Swiatkowski and Dompnier (2017) cite a few reasons. First, they believe that statistical power, or making the decision to not reject the null hypothesis (H0 – hypothesis stating that there is no effect or your hypothesis was not correct) when it is actually false, is an issue in social psychology. Many studies are underpowered as shown by small effect sizes observed in the field, which inflates the rate of false-positive findings and leads to unreplicable findings.

Second, they say that some researchers use “unjustifiable flexibility in data analysis, such as working with several undisclosed dependent variables, collecting more observations after initial hypothesis testing, stopping data collection earlier than planned because of a statistically significant predicted finding, controlling for gender effects a posterior, dropping experimental conditions, and so on” (pg. 114). Some also do undisclosed multiple testing without making adjustments, called p-hacking, or dropping observations to achieve a significance level, called cherry picking . Such practices could explain the high prevalence of false positives in social psychological research.

Third, some current publication standards may promote bad research practices in a few ways. Statistical significance has been set at p = 0.05 as the sine qua non condition for publication. According to Swiattkowski and Dompnier (2017) this leads to dichotomous thinking in terms of the “strict existence and non-existence of an effect” (pg. 115). Also, positive, statistically significant results are more likely to be published than negative, statistically, non-significant results which can be hard to interpret. This bias leads to a structural incentive to seek out positive results. Finally, the authors point out that current editorial standards show a preference for novelty or accepting studies which report new and original psychological effects. This reduces the importance of replications which lack prestige and inspire little interest among researchers. It should also be pointed out that there is a mentality of ‘Publish or perish’ at universities for full time faculty. Those who are prolific and publish often are rewarded with promotions, pay raises, tenure, or prestigious professorships. Also, studies that present highly novel and cool findings are showcased by the media.

The authors state, “In the long run, the lack of a viable falsification procedure seriously undermines the quality of scientific knowledge psychology produces. Without a way to build a cumulative net of well-tested theories and to abandon those that are false, social psychology risks ending up with a confused mixture of both instead”(pg. 117).

For more on this issue, check out the following articles

• 2016 Article in the Atlantic – https://www.theatlantic.com/science/archive/2016/03/psychologys-replication-crisis-cant-be-wished-away/472272/
• 2018 Article in The Atlantic – https://www.theatlantic.com/science/archive/2018/11/psychologys-replication-crisis-real/576223/
• 2018 Article in the Washington Post – https://www.washingtonpost.com/news/speaking-of-science/wp/2018/08/27/researchers-replicate-just-13-of-21-social-science-experiments-published-in-top-journals/?noredirect=on&utm_term=.2a05aff2d7de
• 2018 Article from Science News – https://www.sciencenews.org/blog/science-public/replication-crisis-psychology-science-studies-statistics

2.5.2. Generalizability

Earlier we discussed how researchers want to generalize their findings from the sample to the population, or from a small, representative group to everyone. The problem that plagues social psychology is who makes up our samples. Many social psychological studies are conducted with college students working for course credit (Sears, 1986). They represent what is called a convenience sample . Can we generalize from college students to the larger group?

Module Recap

In Module 1 we stated that psychology studied behavior and mental processes using the strict standards of science. In Module 2 we showed you how that is done via adoption of the scientific method and use of the research designs of observation, case study, surveys, correlation, and experiments. To make sure our measurement of a variable is sound, we need to have measures that are reliable and valid. And to give our research legitimacy we have to use clear ethical standards for research to include gaining informed consent from participants, telling them of the risks, giving them the right to withdraw, debriefing them, and using nothing more than minimal deception. Despite all this, psychology faces a crisis in which many studies are not replicating and findings from some social psychological research are not generalizable to the population.

This concludes Part I of the book. In Part II we will discuss how we think about ourselves and others. First, we will tackle the self and then move to the perception of others. Part II will conclude with a discussion of attitudes.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Chapter 2. Sociological Research

Learning objectives.

2.1. Approaches to Sociological Research

• Define and describe the scientific method
• Explain how the scientific method is used in sociological research
• Understand the difference between positivist and interpretive approaches to the scientific method in sociology
• Define what reliability and validity mean in a research study

2.2. Research Methods

• Differentiate between four kinds of research methods: surveys, experiments, field research, and secondary data and textual analysis
• Understand why different topics are better suited to different research approaches

2.3. Ethical Concerns

• Understand why ethical standards exist
• Demonstrate awareness of the Canadian Sociological Association’s Code of Ethics
• Define value neutrality
• Outline some of the issues of value neutrality in sociology

Introduction to Sociological Research

In the university cafeteria, you set your lunch tray down at a table, grab a chair, join a group of your classmates, and hear the start of two discussions. One person says, “It’s weird how Justin Bieber has 48 million followers on Twitter.” Another says, “Disney World is packed year round.” Those two seemingly benign statements are claims, or opinions, based on everyday observation of human behaviour. Perhaps the speakers had firsthand experience, talked to experts, conducted online research, or saw news segments on TV. In response, two conversations erupt. “I don’t see why anyone would want to go to Disney World and stand in those long lines.” “Are you kidding?! Going to Disney World is one of my favourite childhood memories.” “It’s the opposite for me with Justin Bieber. Seeing people camp out outside his hotel just to get a glimpse of him; it doesn’t make sense.” “Well, you’re not a teenage girl.” “Going to a theme park is way different than trying to see a teenage heart throb.” “But both are things people do for the same reason: they’re looking for a good time.” “If you call getting crushed by a crowd of strangers fun.”

As your classmates at the lunch table discuss what they know or believe, the two topics converge. The conversation becomes a debate. Someone compares Beliebers to Beatles fans. Someone else compares Disney World to a cruise. Students take sides, agreeing or disagreeing, as the conversation veers to topics such as crowd control, mob mentality, political protests, and group dynamics. If you contributed your expanding knowledge of sociological research to this conversation, you might make statements like these: “Justin Bieber’s fans long for an escape from the boredom of real teenage life. Beliebers join together claiming they want romance, except what they really want is a safe place to explore the confusion of teenage sexual feelings.” And this: “Mickey Mouse is a larger-than-life cartoon celebrity. Disney World is a place where families go to see what it would be like to live inside a cartoon.” You finish lunch, clear away your tray, and hurry to your next class. But you are thinking of Justin Bieber and Disney World. You have a new perspective on human behaviour and a list of questions that you want answered. That is the purpose of sociological research—to investigate and provide insights into how human societies function.

Although claims and opinions are part of sociology, sociologists use empirical evidence (that is, evidence corroborated by direct experience and/or observation) combined with the scientific method or an interpretive framework to deliver sound sociological research. They also rely on a theoretical foundation that provides an interpretive perspective through which they can make sense of scientific results. A truly scientific sociological study of the social situations up for discussion in the cafeteria would involve these prescribed steps: defining a specific question, gathering information and resources through observation, forming a hypothesis, testing the hypothesis in a reproducible manner, analyzing and drawing conclusions from the data, publishing the results, and anticipating further development when future researchers respond to and retest findings.

An appropriate starting point in this case might be the question “What do fans of Justin Bieber seek that drives them to follow his Twitter comments so faithfully?” As you begin to think like a sociologist, you may notice that you have tapped into your observation skills. You might assume that your observations and insights are valuable and accurate. But the results of casual observation are limited by the fact that there is no standardization—who is to say one person’s observation of an event is any more accurate than another’s? To mediate these concerns, sociologists rely on systematic research processes.

When sociologists apply the sociological perspective and begin to ask questions, no topic is off limits. Every aspect of human behaviour is a source of possible investigation. Sociologists question the world that humans have created and live in. They notice patterns of behaviour as people move through that world. Using sociological methods and systematic research within the framework of the scientific method and a scholarly interpretive perspective, sociologists have discovered workplace patterns that have transformed industries, family patterns that have enlightened parents, and education patterns that have aided structural changes in classrooms. The students at that university cafeteria discussion put forth a few loosely stated opinions.

If the human behaviours around those claims were tested systematically, a student could write a report and offer the findings to fellow sociologists and the world in general. The new perspective could help people understand themselves and their neighbours and help people make better decisions about their lives. It might seem strange to use scientific practices to study social trends, but, as we shall see, it’s extremely helpful to rely on systematic approaches that research methods provide. Sociologists often begin the research process by asking a question about how or why things happen in this world. It might be a unique question about a new trend or an old question about a common aspect of life. Once a question is formed, a sociologist proceeds through an in-depth process to answer it. In deciding how to design that process, the researcher may adopt a positivist approach or an interpretive approach. The following sections describe these approaches to knowledge.

The Scientific Method

Sociologists make use of tried-and-true methods of research, such as experiments, surveys, field research, and textual analysis. But humans and their social interactions are so diverse that they can seem impossible to chart or explain. It might seem that science is about discoveries and chemical reactions or about proving ideas right or wrong rather than about exploring the nuances of human behaviour. However, this is exactly why scientific models work for studying human behaviour. A scientific process of research establishes parameters that help make sure results are objective and accurate. Scientific methods provide limitations and boundaries that focus a study and organize its results. This is the case for both positivist or quantitative methodologies and interpretive or qualitative methodologies. The scientific method involves developing and testing theories about the world based on empirical evidence. It is defined by its commitment to systematic observation of the empirical world and strives to be objective, critical, skeptical, and logical. It involves a series of prescribed steps that have been established over centuries of scholarship.

But just because sociological studies use scientific methods does not make the results less human. Sociological topics are not reduced to right or wrong facts. In this field, results of studies tend to provide people with access to knowledge they did not have before—knowledge of other cultures, knowledge of rituals and beliefs, knowledge of trends and attitudes. No matter what research approach is used, researchers want to maximize the study’s reliability (how likely research results are to be replicated if the study is reproduced). Reliability increases the likelihood that what is true of one person will be true of all people in a group. Researchers also strive for validity (how well the study measures what it was designed to measure).

Returning to the Disney World topic, reliability of a study would reflect how well the resulting experience represents the average experience of theme park-goers. Validity would ensure that the study’s design accurately examined what it was designed to study, so an exploration of adults’ interactions with costumed mascots should address that issue and not veer into other age groups’ interactions with them or into adult interactions with staff or other guests.

In general, sociologists tackle questions about the role of social characteristics in outcomes. For example, how do different communities fare in terms of psychological well-being, community cohesiveness, range of vocation, wealth, crime rates, and so on? Are communities functioning smoothly? Sociologists look between the cracks to discover obstacles to meeting basic human needs. They might study environmental influences and patterns of behaviour that lead to crime, substance abuse, divorce, poverty, unplanned pregnancies, or illness. And, because sociological studies are not all focused on problematic behaviours or challenging situations, researchers might study vacation trends, healthy eating habits, neighbourhood organizations, higher education patterns, games, parks, and exercise habits.

Sociologists can use the scientific method not only to collect but to interpret and analyze the data. They deliberately apply scientific logic and objectivity. They are interested in but not attached to the results. Their research work is independent of their own political or social beliefs. This does not mean researchers are not critical. Nor does it mean they do not have their own personalities, complete with preferences and opinions. But sociologists deliberately use the scientific method to maintain as much objectivity, focus, and consistency as possible in a particular study. With its systematic approach, the scientific method has proven useful in shaping sociological studies. The scientific method provides a systematic, organized series of steps that help ensure objectivity and consistency in exploring a social problem. They provide the means for accuracy, reliability, and validity. In the end, the scientific method provides a shared basis for discussion and analysis (Merton 1963). Typically, the scientific method starts with these steps—1) ask a question, 2) research existing sources, 3) formulate a hypothesis—described below.

Ask a Question

The first step of the scientific method is to ask a question, describe a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geography and timeframe. “Are societies capable of sustained happiness?” would be too vague. The question should also be broad enough to have universal merit. “What do personal hygiene habits reveal about the values of students at XYZ High School?” would be too narrow. That said, happiness and hygiene are worthy topics to study.

Sociologists do not rule out any topic, but would strive to frame these questions in better research terms. That is why sociologists are careful to define their terms. In a hygiene study, for instance, hygiene could be defined as “personal habits to maintain physical appearance (as opposed to health),” and a researcher might ask, “How do differing personal hygiene habits reflect the cultural value placed on appearance?” When forming these basic research questions, sociologists develop an operational definition ; that is, they define the concept in terms of the physical or concrete steps it takes to objectively measure it. The concept is translated into an observable variable , a measure that has different values. The operational definition identifies an observable condition of the concept.

By operationalizing a variable of the concept, all researchers can collect data in a systematic or replicable manner. The operational definition must be valid in the sense that it is an appropriate and meaningful measure of the concept being studied. It must also be reliable, meaning that results will be close to uniform when tested on more than one person. For example, “good drivers” might be defined in many ways: those who use their turn signals, those who don’t speed, or those who courteously allow others to merge. But these driving behaviours could be interpreted differently by different researchers and could be difficult to measure. Alternatively, “a driver who has never received a traffic violation” is a specific description that will lead researchers to obtain the same information, so it is an effective operational definition.

Research Existing Sources

The next step researchers undertake is to conduct background research through a literature review , which is a review of any existing similar or related studies. A visit to the library and a thorough online search will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted on the topic at hand and enables them to position their own research to build on prior knowledge. It allows them to sharpen the focus of their research question and avoid duplicating previous research. Researchers—including student researchers—are responsible for correctly citing existing sources they use in a study or that inform their work. While it is fine to build on previously published material (as long as it enhances a unique viewpoint), it must be referenced properly and never plagiarized. To study hygiene and its value in a particular society, a researcher might sort through existing research and unearth studies about childrearing, vanity, obsessive-compulsive behaviours, and cultural attitudes toward beauty. It’s important to sift through this information and determine what is relevant. Using existing sources educates a researcher and helps refine and improve a study’s design.

Formulate a Hypothesis

A hypothesis is an assumption about how two or more variables are related; it makes a conjectural statement about the relationship between those variables. It is an “educated guess” because it is not random but based on theory, observations, patterns of experience, or the existing literature. The hypothesis formulates this guess in the form of a testable proposition. However, how the hypothesis is handled differs between the positivist and interpretive approaches. Positivist methodologies are often referred to as hypothetico-deductive methodologies . A hypothesis is derived from a theoretical proposition. On the basis of the hypothesis a prediction or generalization is logically deduced. In positivist sociology, the hypothesis predicts how one form of human behaviour influences another.

Successful prediction will determine the adequacy of the hypothesis and thereby test the theoretical proposition. Typically positivist approaches operationalize variables as quantitative data ; that is, by translating a social phenomenon like “health” into a quantifiable or numerically measurable variable like “number of visits to the hospital.” This permits sociologists to formulate their predictions using mathematical language like regression formulas, to present research findings in graphs and tables, and to perform mathematical or statistical techniques to demonstrate the validity of relationships.

Variables are examined to see if there is a correlation between them. When a change in one variable coincides with a change in another variable there is a correlation. This does not necessarily indicate that changes in one variable causes a change in another variable, however, just that they are associated. A key distinction here is between independent and dependent variables. In research, independent variables are the cause of the change. The dependent variable is the effect , or thing that is changed. For example, in a basic study, the researcher would establish one form of human behaviour as the independent variable and observe the influence it has on a dependent variable. How does gender (the independent variable) affect rate of income (the dependent variable)? How does one’s religion (the independent variable) affect family size (the dependent variable)? How is social class (the dependent variable) affected by level of education (the independent variable)? For it to become possible to speak about causation, three criteria must be satisfied:

• There must be a relationship or correlation between the independent and dependent variables.
• The independent variable must be prior to the dependent variable.
• There must be no other intervening variable responsible for the causal relationship.

 Table 2.1. Examples of Dependent and Independent Variables Typically, the independent variable causes the dependent variable to change in some way.

At this point, a researcher’s operational definitions help measure the variables. In a study asking how tutoring improves grades, for instance, one researcher might define “good” grades as a C or better, while another uses a B+ as a starting point for “good.” Another operational definition might describe “tutoring” as “one-on-one assistance by an expert in the field, hired by an educational institution.” Those definitions set limits and establish cut-off points, ensuring consistency and replicability in a study. As the chart shows, an independent variable is the one that causes a dependent variable to change. For example, a researcher might hypothesize that teaching children proper hygiene (the independent variable) will boost their sense of self-esteem (the dependent variable). Or rephrased, a child’s sense of self-esteem depends, in part, on the quality and availability of hygienic resources.

Of course, this hypothesis can also work the other way around. Perhaps a sociologist believes that increasing a child’s sense of self-esteem (the independent variable) will automatically increase or improve habits of hygiene (now the dependent variable). Identifying the independent and dependent variables is very important. As the hygiene example shows, simply identifying two topics, or variables, is not enough: Their prospective relationship must be part of the hypothesis. Just because a sociologist forms an educated prediction of a study’s outcome doesn’t mean data contradicting the hypothesis are not welcome. Sociologists analyze general patterns in response to a study, but they are equally interested in exceptions to patterns.

In a study of education, a researcher might predict that high school dropouts have a hard time finding a rewarding career. While it has become at least a cultural assumption that the higher the education, the higher the salary and degree of career happiness, there are certainly exceptions. People with little education have had stunning careers, and people with advanced degrees have had trouble finding work. A sociologist prepares a hypothesis knowing that results will vary.

While many sociologists rely on the positivist hypothetico-deductive method in their research, others operate from an interpretive approach . While systematic, this approach does not follow the hypothesis-testing model that seeks to make generalizable predictions from quantitative variables. Instead, an interpretive framework seeks to understand social worlds from the point of view of participants, leading to in-depth knowledge. It focuses on qualitative data, or the meanings that guide people’s behaviour. Rather than relying on quantitative instruments like questionnaires or experiments, which can be artificial, the interpretive approach attempts to find ways to get closer to the informants’ lived experience and perceptions. Interpretive research is generally more descriptive or narrative in its findings. It can begin from a deductive approach, by deriving a hypothesis from theory and then seeking to confirm it through methodologies like in-depth interviews.

However, it is ideally suited to an inductive approach in which the hypothesis emerges only after a substantial period of direct observation or interaction with subjects. This type of approach is exploratory in that the researcher also learns as he or she proceeds, sometimes adjusting the research methods or processes midway to respond to new insights and findings as they evolve. Once the preliminary work is done, it’s time for the next research steps: designing and conducting a study, and drawing conclusions. These research methods are discussed below.

Sociologists examine the world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study—perhaps a positivist, quantitative method for conducting research and obtaining data, or perhaps an ethnographic study utilizing an interpretive framework. Planning the research design is a key step in any sociological study. When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher would not stroll into a crime-ridden neighbourhood at midnight, calling out, “Any gang members around?” And if a researcher walked into a coffee shop and told the employees they would be observed as part of a study on work efficiency, the self-conscious, intimidated baristas might not behave naturally.

In the 1920s, leaders of a Chicago factory called Hawthorne Works commissioned a study to determine whether or not changing certain aspects of working conditions could increase or decrease worker productivity. Sociologists were surprised when the productivity of a test group increased when the lighting of their workspace was improved. They were even more surprised when productivity improved when the lighting of the workspace was dimmed. In fact almost every change of independent variable—lighting, breaks, work hours—resulted in an improvement of productivity. But when the study was over, productivity dropped again.

Why did this happen? In 1953, Henry A. Landsberger analyzed the study results to answer this question. He realized that employees’ productivity increased because sociologists were paying attention to them. The sociologists’ presence influenced the study results. Worker behaviours were altered not by the lighting but by the study itself. From this, sociologists learned the importance of carefully planning their roles as part of their research design (Franke and Kaul 1978). Landsberger called the workers’ response the Hawthorne effect —people changing their behaviour because they know they are being watched as part of a study.

The Hawthorne effect is unavoidable in some research. In many cases, sociologists have to make the purpose of the study known for ethical reasons. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985). Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviours, early education, or the Ku Klux Klan. Researchers cannot just stroll into prisons, kindergarten classrooms, or Ku Klux Klan meetings and unobtrusively observe behaviours. In situations like these, other methods are needed. All studies shape the research design, while research design simultaneously shapes the study. Researchers choose methods that best suit their study topic and that fit with their overall goal for the research.

In planning a study’s design, sociologists generally choose from four widely used methods of social investigation: survey, experiment, field research, and textual or secondary data analysis (or use of existing sources). Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviours and opinions, often in the form of a questionnaire. The survey is one of the most widely used positivist research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

At some point or another, everyone responds to some type of survey. The Statistics Canada census is an excellent example of a large-scale survey intended to gather sociological data. Customers also fill out questionnaires at stores or promotional events, responding to questions such as “How did you hear about the event?” and “Were the staff helpful?” You’ve probably picked up the phone and heard a caller ask you to participate in a political poll or similar type of survey: “Do you eat hot dogs? If yes, how many per month?” Not all surveys would be considered sociological research. Marketing polls help companies refine marketing goals and strategies; they are generally not conducted as part of a scientific study, meaning they are not designed to test a hypothesis or to contribute knowledge to the field of sociology. The results are not published in a refereed scholarly journal, where design, methodology, results, and analyses are vetted.

Often, polls on TV do not reflect a general population, but are merely answers from a specific show’s audience. Polls conducted by programs such as American Idol or Canadian Idol represent the opinions of fans but are not particularly scientific. A good contrast to these are the BBM Ratings, which determine the popularity of radio and television programming in Canada through scientific market research. Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel and think—or at least how they say they feel and think. Surveys can track attitudes and opinions, political preferences, reported individual behaviours (such as sleeping, driving, or texting habits), or factual information such as employment status, income, and education levels. A survey targets a specific population , people who are the focus of a study, such as university athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes.

Most researchers choose to survey a small sector of the population, or a sample : that is, a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. According to the laws of probability, random samples represent the population as a whole. For instance, an Ipsos Reid poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people. However the validity of surveys can be threatened when part of the population is inadvertently excluded from the sample (e.g., telephone surveys that rely on land lines exclude people that use only cell phones) or when there is a low response rate. After selecting subjects, the researcher develops a specific plan to ask questions and record responses.

It is important to inform subjects of the nature and purpose of the study upfront. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument (a means of gathering the information). A common instrument is a structured questionnaire, in which subjects answer a series of set questions. For some topics, the researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question.

This kind of quantitative data —research collected in numerical form that can be counted—is easy to tabulate. Just count up the number of “yes” and “no” answers or tabulate the scales of “strongly agree,” “agree,” disagree,” etc. responses and chart them into percentages. This is also their chief drawback however: their artificiality. In real life, there are rarely any unambiguously yes-or-no answers. Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” “agree,” “strongly agree,” or an option next to a checkbox. In those cases, the answers are subjective, varying from person to person. How do you plan to use your university education? Why do you follow Justin Bieber around the country and attend every concert? Those types of questions require short essay responses, and participants willing to take the time to write those answers will convey personal information about religious beliefs, political views, and morals.

Some topics that reflect internal thought are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of information is qualitative data —results that are subjective and often based on what is seen in a natural setting. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and is a way of conducting surveys on a topic. Interviews are similar to the short answer questions on surveys in that the researcher asks subjects a series of questions. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly. Questions such as “How did society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. And, obviously, a sociological interview is not an interrogation. The researcher will benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Experiments

You’ve probably tested personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis. One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach. There are two main types of experiments: lab-based experiments and natural or field experiments.

In a lab setting, the research can be controlled so that perhaps more data can be recorded in a certain amount of time. In a natural or field-based experiment, the generation of data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher. As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens, then another particular thing will result.

To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables. Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group . The experimental group is exposed to the independent variable(s) and the control group is not. This is similar to pharmaceutical drug trials in which the experimental group is given the test drug and the control group is given a placebo or sugar pill. To test the benefits of tutoring, for example, the sociologist might expose the experimental group of students to tutoring while the control group does not receive tutoring. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record, for example.

The Stanford Prison Experiment is perhaps one of the most famous sociological experiments ever conducted. In 1971, 24 healthy, middle-class male university students were selected to take part in a simulated jail environment to examine the effects of social setting and social roles on individual psychology and behaviour. They were randomly divided into 12 guards and 12 prisoners. The prisoner subjects were arrested at home and transported blindfolded to the simulated prison in the basement of the psychology building on the campus of Stanford University. Within a day of arriving the prisoners and the guards began to display signs of trauma and sadism respectively. After some prisoners revolted by blockading themselves in their cells, the guards resorted to using increasingly humiliating and degrading tactics to control the prisoners through psychological manipulation. The experiment had to be abandoned after only six days because the abuse had grown out of hand (Haney, Banks, and Zimbardo 1973). While the insights into the social dynamics of authoritarianism it generated were fascinating, the Stanford Prison Experiment also serves as an example of the ethical issues that emerge when experimenting on human subjects.

Making Connections: Sociological Research

An experiment in action: mincome.

A real-life example will help illustrate the experimental process in sociology. Between 1974 and 1979 an experiment was conducted in the small town of Dauphin, Manitoba (the “garden capital of Manitoba”). Each family received a modest monthly guaranteed income—a “mincome”—equivalent to a maximum of 60 percent of the “low-income cut-off figure” (a Statistics Canada measure of poverty, which varies with family size). The income was 50 cents per dollar less for families who had incomes from other sources. Families earning over a certain income level did not receive mincome. Families that were already collecting welfare or unemployment insurance were also excluded. The test families in Dauphin were compared with control groups in other rural Manitoba communities on a range of indicators such as number of hours worked per week, school performance, high school dropout rates, and hospital visits (Forget 2011). A guaranteed annual income was seen at the time as a less costly, less bureaucratic public alternative for addressing poverty than the existing employment insurance and welfare programs. Today it is an active proposal being considered in Switzerland (Lowrey 2013).

Intuitively, it seems logical that lack of income is the cause of poverty and poverty-related issues. One of the main concerns, however, was whether a guaranteed income would create a disincentive to work. The concept appears to challenge the principles of the Protestant work ethic (see the discussion of Max Weber in Chapter 1). The study did find very small decreases in hours worked per week: about 1 percent for men, 3 percent for wives, and 5 percent for unmarried women. Forget (2011) argues this was because the income provided an opportunity for people to spend more time with family and school, especially for young mothers and teenage girls. There were also significant social benefits from the experiment, including better test scores in school, lower high school dropout rates, fewer visits to hospital, fewer accidents and injuries, and fewer mental health issues.

Ironically, due to lack of guaranteed funding (and lack of political interest by the late 1970s), the data and results of the study were not analyzed or published until 2011. The data were archived and sat gathering dust in boxes. The mincome experiment demonstrated the benefits that even a modest guaranteed annual income supplement could have on health and social outcomes in communities. People seem to live healthier lives and get a better education when they do not need to worry about poverty. In her summary of the research, Forget notes that the impact of the income supplement was surprisingly large given that at any one time only about a third of the families were receiving the income and, for some families, the income amount would have been very small. The income benefit was largest for low-income working families but the research showed that the entire community profited. The improvement in overall health outcomes for the community suggest that a guaranteed income would also result in savings for the public health system.

Field Research

The work of sociology rarely happens in limited, confined spaces. Sociologists seldom study subjects in their own offices or laboratories. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment without doing a lab experiment or a survey. It is a research method suited to an interpretive approach rather than to positivist approaches. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In fieldwork, the sociologists, rather than the subjects, are the ones out of their element. The researcher interacts with or observes a person or people, gathering data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or a care home, a hospital, airport, mall, or beach resort.

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviours in that setting. Fieldwork is optimal for observing how people behave. It is less useful, however, for developing causal explanations of why they behave that way. From the small size of the groups studied in fieldwork, it is difficult to make predictions or generalizations to a larger population. Similarly, there are difficulties in gaining an objective distance from research subjects. It is difficult to know whether another researcher would see the same things or record the same data. We will look at three types of field research: participant observation, ethnography, and the case study.

Making Connections: Sociology in the Real World

When is sharing not such a good idea.

Choosing a research methodology depends on a number of factors, including the purpose of the research and the audience for whom the research is intended. If we consider the type of research that might go into producing a government policy document on the effectiveness of safe injection sites for reducing the public health risks of intravenous drug use, we would expect public administrators to want “hard” (i.e., quantitative) evidence of high reliability to help them make a policy decision. The most reliable data would come from an experimental or quasi-experimental research model in which a control group can be compared with an experimental group using quantitative measures.

This approach has been used by researchers studying InSite in Vancouver (Marshall et al. 2011; Wood et al. 2006). InSite is a supervised safe-injection site where heroin addicts and other intravenous drug users can go to inject drugs in a safe, clean environment. Clean needles are provided and health care professionals are on hand to intervene in the case of overdose or other medical emergency. It is a controversial program both because heroin use is against the law (the facility operates through a federal ministerial exemption) and because the heroin users are not obliged to quit using or seek therapy. To assess the effectiveness of the program, researchers compared the risky usage of drugs in populations before and after the opening of the facility and geographically near and distant to the facility. The results from the studies have shown that InSite has reduced both deaths from overdose and risky behaviours, such as the sharing of needles, without increasing the levels of crime associated with drug use and addiction.

On the other hand, if the research question is more exploratory (for example, trying to discern the reasons why individuals in the crack smoking subculture engage in the risky activity of sharing pipes), the more nuanced approach of fieldwork is more appropriate. The research would need to focus on the subcultural context, rituals, and meaning of sharing pipes, and why these phenomena override known health concerns. Graduate student Andrew Ivsins at the University of Victoria studied the practice of sharing pipes among 13 habitual users of crack cocaine in Victoria, B.C. (Ivsins 2010). He met crack smokers in their typical setting downtown and used an unstructured interview method to try to draw out the informal norms that lead to sharing pipes. One factor he discovered was the bond that formed between friends or intimate partners when they shared a pipe. He also discovered that there was an elaborate subcultural etiquette of pipe use that revolved around the benefit of getting the crack resin smokers left behind. Both of these motives tended to outweigh the recognized health risks of sharing pipes (such as hepatitis) in the decision making of the users. This type of research was valuable in illuminating the unknown subcultural norms of crack use that could still come into play in a harm reduction strategy such as distributing safe crack kits to addicts.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see if anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a sociologist will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers study a naturally occurring social activity without imposing artificial or intrusive research devices, like fixed questionnaire questions, onto the situation. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behaviour. Researchers temporarily put themselves into “native” roles and record their observations. A researcher might work as a waitress in a diner, or live as a homeless person for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside. Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in shaping data into results. In a study of small-town America conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in American towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised their purpose. This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd and Lynd 1959).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviours of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behaviour. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job. Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book, describing what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, as the story goes, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study. To her surprise, her editor responded, Why don’t you do it? That is how Ehrenreich found herself joining the ranks of the low-wage service sector. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter. She discovered the obvious: that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle- and upper-class people never think about. She witnessed firsthand the treatment of service work employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

Ethnography

Ethnography is the extended observation of the social perspective and cultural values of an entire social setting. Researchers seek to immerse themselves in the life of a bounded group, by living and working among them. Often ethnography involves participant observation, but the focus is the systematic observation of an entire community.

The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a community. An ethnographic study might observe, for example, a small Newfoundland fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or Disney World. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible, and keeping careful notes on his or her observations.

A sociologist studying a tribe in the Amazon might learn the language, watch the way villagers go about their daily lives, ask individuals about the meaning of different aspects of activity, study the group’s cosmology and then write a paper about it. To observe a spiritual retreat centre, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record how people experience spirituality in this setting, and collate the material into results.

The Feminist Perspective: Institutional Ethnography

Dorothy Smith elaborated on traditional ethnography to develop what she calls institutional ethnography (2005). In modern society the practices of everyday life in any particular local setting are often organized at a level that goes beyond what an ethnographer might observe directly. Everyday life is structured by “extralocal,” institutional forms; that is, by the practices of institutions that act upon people from a distance. It might be possible to conduct ethnographic research on the experience of domestic abuse by living in a women’s shelter and directly observing and interviewing victims to see how they form an understanding of their situation. However, to the degree that the women are seeking redress through the criminal justice system a crucial element of the situation would be missing. In order to activate a response from the police or the courts, a set of standard legal procedures must be followed, a “case file” must be opened, legally actionable evidence must be established, forms filled out, etc. All of this allows criminal justice agencies to organize and coordinate the response.

The urgent and immediate experience of the domestic abuse victims needs to be translated into a format that enables distant authorities to take action. Often this is a frustrating and mysterious process in which the immediate needs of individuals are neglected so that needs of institutional processes are met. Therefore to research the situation of domestic abuse victims, an ethnography needs to somehow operate at two levels: the close examination of the local experience of particular women and the simultaneous examination of the extralocal, institutional world through which their world is organized. In order to accomplish this, institutional ethnography focuses on the study of the way everyday life is coordinated through “textually mediated” practices: the use of written documents, standardized bureaucratic categories, and formalized relationships (Smith 1990).

Institutional paperwork translates the specific details of locally lived experience into a standardized format that enables institutions to apply the institution’s understandings, regulations, and operations in different local contexts. The study of these textual practices reveal otherwise inaccessible processes that formal organizations depend on: their formality, their organized character, and their ongoing methods of coordination, etc. An institutional ethnography often begins by following the paper trail that emerges when people interact with institutions: how does a person formulate a narrative about what has happened to him or her in a way that the institution will recognize? How is it translated into the abstract categories on a form or screen that enable an institutional response to be initiated? What is preserved in the translation to paperwork and what is lost? Where do the forms go next? What series of “processing interchanges” take place between different departments or agencies through the circulation of paperwork? How is the paperwork modified and made actionable through this process (e.g., an incident report, warrant request, motion for continuance)?

Smith’s insight is that the shift from the locally lived experience of individuals to the extralocal world of institutions is nothing short of a radical metaphysical shift in worldview. In institutional worlds, meanings are detached from directly lived processes and reconstituted in an organizational time, space, and consciousness that is fundamentally different from their original reference point. For example, the crisis that has led to a loss of employment becomes a set of anonymous criteria that determines one’s eligibility for Employment Insurance.

The unique life of a disabled child becomes a checklist that determines the content of an “individual education program” in the school system, which in turn determines whether funding will be provided for special aid assistants or therapeutic programs. Institutions put together a picture of what has occurred that is not at all the same as what was lived. The ubiquitous but obscure mechanism by which this is accomplished is textually mediated communication . The goal of institutional ethnography therefore is to making “documents or texts visible as constituents of social relations” (Smith 1990). Institutional ethnography is very useful as a critical research strategy. It is an analysis that gives grassroots organizations, or those excluded from the circles of institutional power, a detailed knowledge of how the administrative apparatuses actually work. This type of research enables more effective actions and strategies for change to be pursued.

The Case Study

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation, and even participant observation, if possible. Researchers might use this method to study a single case of, for example, a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that a developed study of a single case, while offering depth on a topic, does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can add tremendous knowledge to a certain discipline. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, elements crucial to a “civilized” child’s development. These children mimic the behaviours and movements of animals, and often invent their own language. There are only about 100 cases of “feral children” in the world. As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” child development. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject. At age three, a Ukrainian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, eating raw meat and scraps. Five years later, a neighbour called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviours, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2006). Case studies like this offer a way for sociologists to collect data that may not be collectable by any other method.

Secondary Data or Textual Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data or textual analysis . Secondary data do not result from firsthand research collected from primary sources, but are drawn from the already-completed work of other researchers. Sociologists might study texts written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines from any period in history. Using available information not only saves time and money, but it can add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behaviour and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or Facebook.

One methodology that sociologists employ with secondary data is content analysis. Content analysis is a quantitative approach to textual research that selects an item of textual content (i.e., a variable) that can be reliably and consistently observed and coded, and surveys the prevalence of that item in a sample of textual output. For example, Gilens (1996) wanted to find out why survey research shows that the American public substantially exaggerates the percentage of African Americans among the poor. He examined whether media representations influence public perceptions and did a content analysis of photographs of poor people in American news magazines. He coded and then systematically recorded incidences of three variables: (1) Race: white, black, indeterminate; (2) Employed: working, not working; and (3) Age. Gilens discovered that not only were African Americans markedly overrepresented in news magazine photographs of poverty, but that the photos also tended to underrepresent “sympathetic” subgroups of the poor—the elderly and working poor—while overrepresenting less sympathetic groups—unemployed, working age adults. Gilens concluded that by providing a distorted representation of poverty, U.S. news magazines “reinforce negative stereotypes of blacks as mired in poverty and contribute to the belief that poverty is primarily a ‘black problem’” (1996).

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like Statistics Canada or the World Health Organization, publish studies with findings that are useful to sociologists. A public statistic that measures inequality of incomes might be useful for studying who benefited and who lost as a result of the 2008 recession; a demographic profile of different immigrant groups might be compared with data on unemployment to examine the reasons why immigration settlement programs are more effective for some communities than for others. One of the advantages of secondary data is that it is nonreactive (or unobtrusive) research, meaning that it does not include direct contact with subjects and will not alter or influence people’s behaviours. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process. Using available data does have its challenges. Public records are not always easy to access. A researcher needs to do some legwork to track them down and gain access to records. In some cases there is no way to verify the accuracy of existing data. It is easy, for example, to count how many drunk drivers are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not include the precise angle the researcher seeks. For example, the salaries paid to professors at universities is often published. But the separate figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they have been teaching. In his research, sociologist Richard Sennett uses secondary data to shed light on current trends. In The Craftsman (2008), he studied the human desire to perform quality work, from carpentry to computer programming. He studied the line between craftsmanship and skilled manual labour. He also studied changes in attitudes toward craftsmanship that occurred not only during and after the Industrial Revolution, but also in ancient times. Obviously, he could not have firsthand knowledge of periods of ancient history; he had to rely on secondary data for part of his study. When conducting secondary data or textual analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, Robert S. Lynd and Helen Merrell Lynd gathered research for their book Middletown: A Study in Modern American Culture in the 1920s. Attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal the truth about small American communities. Today, it is an illustration of 1920s attitudes and values.

Sociologists conduct studies to shed light on human behaviours. Knowledge is a powerful tool that can be used toward positive change. And while a sociologist’s goal is often simply to uncover knowledge rather than to spur action, many people use sociological studies to help improve people’s lives. In that sense, conducting a sociological study comes with a tremendous amount of responsibility. Like any researchers, sociologists must consider their ethical obligation to avoid harming subjects or groups while conducting their research. The Canadian Sociological Association, or CSA, is the major professional organization of sociologists in Canada. The CSA is a great resource for students of sociology as well.

The CSA maintains a code of ethics —formal guidelines for conducting sociological research—consisting of principles and ethical standards to be used in the discipline. It also describes procedures for filing, investigating, and resolving complaints of unethical conduct. These are in line with the Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans (2010) , which applies to any research with human subjects funded by one of the three federal research agencies – the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC).

Practising sociologists and sociology students have a lot to consider. Some of the guidelines state that researchers must try to be skillful and fair-minded in their work, especially as it relates to their human subjects. Researchers must obtain participants’ informed consent, and inform subjects of the responsibilities and risks of research before they agree to participate. During a study, sociologists must ensure the safety of participants and immediately stop work if a subject becomes potentially endangered on any level. Researchers are required to protect the privacy of research participants whenever possible. Even if pressured by authorities, such as police or courts, researchers are not ethically allowed to release confidential information. Researchers must make results available to other sociologists, must make public all sources of financial support, and must not accept funding from any organization that might cause a conflict of interest or seek to influence the research results for its own purposes. The CSA’s ethical considerations shape not only the study but also the publication of results.

Pioneer German sociologist Max Weber (1864–1920) identified another crucial ethical concern. Weber understood that personal values could distort the framework for disclosing study results. While he accepted that some aspects of research design might be influenced by personal values, he declared it was entirely inappropriate to allow personal values to shape the interpretation of the responses. Sociologists, he stated, must establish value neutrality , a practice of remaining impartial, without bias or judgment, during the course of a study and in publishing results (1949). Sociologists are obligated to disclose research findings without omitting or distorting significant data. Value neutrality does not mean having no opinions. It means striving to overcome personal biases, particularly subconscious biases, when analyzing data. It means avoiding skewing data in order to match a predetermined outcome that aligns with a particular agenda, such as a political or moral point of view. Investigators are ethically obligated to report results, even when they contradict personal views, predicted outcomes, or widely accepted beliefs. Is value neutrality possible?

Many sociologists believe it is impossible to set aside personal values and retain complete objectivity. Individuals inevitably see the world from a partial perspective. Their interests are central to the types of topics they choose, the types of questions they ask, the way they frame their research and the research methodologies they select to pursue it. Moreover, facts, however objective, do not exist in a void. As we noted in Chapter 1, Jürgen Habermas (1972) argues that sociological research has built-in interests quite apart from the personal biases of individual researchers. Positivist sociology has an interest in pursuing types of knowledge that are useful for controlling and administering social life. Interpretive sociology has an interest in pursuing types of knowledge that promote greater mutual understanding and the possibility of consensus among members of society. Critical sociology has an interest in types of knowledge that enable emancipation from power relations and forms of domination in society. In Habermas’ view, sociological knowledge is not disinterested knowledge. This does not discredit the results of sociological research but allows readers to take into account the perspective of the research when judging the validity and applicability of its outcomes.

case study in-depth analysis of a single event, situation, or individual

code of ethics a set of guidelines that the Canadian Sociological Association has established to foster ethical research and professionally responsible scholarship in sociology

content analysis a quantitative approach to textual research that selects an item of textual content that can be reliably and consistently observed and coded, and surveys the prevalence of that item in a sample of textual output

control group an experimental group that is not exposed to the independent variable

correlation when a change in one variable coincides with a change in another variable, but does not necessarily indicate causation

d ependent variable variable changed by another variable

empirical evidence evidence corroborated by direct experience and/or observation

ethnography observing a complete social setting and all that it entails

experiment the testing of a hypothesis under controlled conditions

field research gathering data from a natural environment without doing a lab experiment or a survey

Hawthorne effect when study subjects behave in a certain manner due to their awareness of being observed by a researcher

hypothesis an educated guess with predicted outcomes about the relationship between two or more variables hypothetico-deductive methodologies methodologies based on deducing a prediction from a hypothesis and testing the  validity of the hypothesis by whether it correctly predicts observations

independent variable  variable that causes change in a dependent variable

inductive approach methodologies that derive a general statement from a series of empirical observations

institutional ethnography the study of the way everyday life is coordinated through institutional, textually mediated practices

interpretive approach a sociological research approach that seeks in-depth understanding of a topic or subject through observation or interaction

interview  a one-on-one conversation between a researcher and a subject

literature review a scholarly research step that entails identifying and studying all existing studies on a topic to create a basis for new research

nonreactive  unobtrusive research that does not include direct contact with subjects and will not alter or influence people’s behaviours

operational definitions specific explanations of abstract concepts that a researcher plans to study

participant observation immersion by a researcher in a group or social setting in order to make observations from an “insider” perspective

population a defined group serving as the subject of a study

positivist approach a research approach based on the natural science model of knowledge utilizing a hypothetico-deductive formulation of the research question and quantitative data

primary data data collected directly from firsthand experience

qualitative data  information based on interpretations of meaning

quantitative data information from research collected in numerical form that can be counted

random sample a study’s participants being randomly selected to serve as a representation of a larger population reliability a measure of a study’s consistency that considers how likely results are to be replicated if a study is reproduced research design a detailed, systematic method for conducting research and obtaining data

sample small, manageable number of subjects that represent the population

scientific method a systematic research method that involves asking a question, researching existing sources, forming a hypothesis, designing and conducting a study, and drawing conclusions

secondary data analysis using data collected by others but applying new interpretations

surveys data collections from subjects who respond to a series of questions about behaviours and opinions, often in the form of a questionnaire

textually mediated communication institutional forms of communication that rely on written documents, texts, and paperwork

validity the degree to which a sociological measure accurately reflects the topic of study

value neutrality a practice of remaining impartial, without bias or judgment during the course of a study and in publishing results

variable a characteristic or measure of a social phenomenon that can take different values

Section Summary

2.1. Approaches to Sociological Research Using the scientific method, a researcher conducts a study in five phases: asking a question, researching existing sources, formulating a hypothesis, conducting a study, and drawing conclusions. The scientific method is useful in that it provides a clear method of organizing a study. Some sociologists conduct scientific research through a positivist framework utilizing a hypothetico-deductive formulation of the research question. Other sociologists conduct scientific research by employing an interpretive framework that is often inductive in nature. Scientific sociological studies often observe relationships between variables. Researchers study how one variable changes another. Prior to conducting a study, researchers are careful to apply operational definitions to their terms and to establish dependent and independent variables.

2.2. Research Methods Sociological research is a fairly complex process. As you can see, a lot goes into even a simple research design. There are many steps and much to consider when collecting data on human behaviour, as well as in interpreting and analyzing data in order to form conclusive results. Sociologists use scientific methods for good reason. The scientific method provides a system of organization that helps researchers plan and conduct the study while ensuring that data and results are reliable, valid, and objective. The many methods available to researchers—including experiments, surveys, field studies, and secondary data analysis—all come with advantages and disadvantages. The strength of a study can depend on the choice and implementation of the appropriate method of gathering research. Depending on the topic, a study might use a single method or a combination of methods. It is important to plan a research design before undertaking a study. The information gathered may in itself be surprising, and the study design should provide a solid framework in which to analyze predicted and unpredicted data.

Table 2.2. Main Sociological Research Methods. Sociological research methods have advantages and disadvantages.

2.3. Ethical Concerns Sociologists and sociology students must take ethical responsibility for any study they conduct. They must first and foremost guarantee the safety of their participants. Whenever possible, they must ensure that participants have been fully informed before consenting to be part of a study. The CSA (Canadian Sociological Association) maintains ethical guidelines that sociologists must take into account as they conduct research. The guidelines address conducting studies, properly using existing sources, accepting funding, and publishing results. Sociologists must try to maintain value neutrality. They must gather and analyze data objectively, setting aside their personal preferences, beliefs, and opinions. They must report findings accurately, even if they contradict personal convictions.

Section Quiz

2.1. Approaches to Sociological Research 1. A measurement is considered ______­ if it actually measures what it is intended to measure, according to the topic of the study.

• sociological
• quantitative

2. Sociological studies test relationships in which change in one ______ causes change in another.

• test subject
• operational definition

3. In a study, a group of 10-year-old boys are fed doughnuts every morning for a week and then weighed to see how much weight they gained. Which factor is the dependent variable?

• the doughnuts
• the duration of a week
• the weight gained

4. Which statement provides the best operational definition of “childhood obesity”?

• children who eat unhealthy foods and spend too much time watching television and playing video games
• a distressing trend that can lead to health issues including type 2 diabetes and heart disease
• body weight at least 20 percent higher than a healthy weight for a child of that height
• the tendency of children today to weigh more than children of earlier generations

2.2. Research Methods 5. Which materials are considered secondary data?

• photos and letters given to you by another person
• books and articles written by other authors about their studies
• information that you have gathered and now have included in your results
• responses from participants whom you both surveyed and interviewed

6. What method did Andrew Ivsins use to study crack users in Victoria?

• field research
• content analysis

7. Why is choosing a random sample an effective way to select participants?

• Participants do not know they are part of a study
• The researcher has no control over who is in the study
• It is larger than an ordinary sample
• Everyone has the same chance of being part of the study

8. What research method did John S. Lynd and Helen Merrell Lynd mainly use in their Middletown study?

• secondary data
• participant observation

9. Which research approach is best suited to the positivist approach?

• questionnaire
• ethnography
• secondary data analysis

10. The main difference between ethnography and other types of participant observation is:

• ethnography isn’t based on hypothesis testing
• ethnography subjects are unaware they’re being studied
• ethnographic studies always involve minority ethnic groups
• there is no difference

11. Which best describes the results of a case study?

• it produces more reliable results than other methods because of its depth
• its results are not generally applicable
• it relies solely on secondary data analysis
• all of the above

12. Using secondary data is considered an unobtrusive or ________ research method.

• nonreactive
• nonparticipatory
• nonrestrictive
• nonconfrontive

2.3. Ethical Concerns 13. Which statement illustrates value neutrality?

• Obesity in children is obviously a result of parental neglect and, therefore, schools should take a greater role to prevent it.
• In 2003, states like Arkansas adopted laws requiring elementary schools to remove soft drink vending machines from schools.
• Merely restricting children’s access to junk food at school is not enough to prevent obesity.
• Physical activity and healthy eating are a fundamental part of a child’s education.

14. Which person or organization defined the concept of value neutrality?

• Institutional Review Board (IRB)
• Peter Rossi
• Canadian Sociological Association (CSA)

15. To study the effects of fast food on lifestyle, health, and culture, from which group would a researcher ethically be unable to accept funding?

• a fast-food restaurant
• a nonprofit health organization
• a private hospital
• a governmental agency like Health and Social Services

Short Answer

• Write down the first three steps of the scientific method. Think of a broad topic that you are interested in and which would make a good sociological study—for example, ethnic diversity in a college, homecoming rituals, athletic scholarships, or teen driving. Now, take that topic through the first steps of the process. For each step, write a few sentences or a paragraph: 1) Ask a question about the topic. 2) Do some research and write down the titles of some articles or books you’d want to read about the topic. 3) Formulate a hypothesis.

2.2.Research Methods

• What type of data do surveys gather? For what topics would surveys be the best research method? What drawbacks might you expect to encounter when using a survey? To explore further, ask a research question and write a hypothesis. Then create a survey of about six questions relevant to the topic. Provide a rationale for each question. Now define your population and create a plan for recruiting a random sample and administering the survey.
• Imagine you are about to do field research in a specific place for a set time. Instead of thinking about the topic of study itself, consider how you, as the researcher, will have to prepare for the study. What personal, social, and physical sacrifices will you have to make? How will you manage your personal effects? What organizational equipment and systems will you need to collect the data?
• Create a brief research design about a topic in which you are passionately interested. Now write a letter to a philanthropic or grant organization requesting funding for your study. How can you describe the project in a convincing yet realistic and objective way? Explain how the results of your study will be a relevant contribution to the body of sociological work already in existence.
• Why do you think the CSA crafted such a detailed set of ethical principles? What type of study could put human participants at risk? Think of some examples of studies that might be harmful. Do you think that, in the name of sociology, some researchers might be tempted to cross boundaries that threaten human rights? Why?
• Would you willingly participate in a sociological study that could potentially put your health and safety at risk, but had the potential to help thousands or even hundreds of thousands of people? For example, would you participate in a study of a new drug that could cure diabetes or cancer, even if it meant great inconvenience and physical discomfort for you or possible permanent damage?

Further Research

2.1. Approaches to Sociological Research For a historical perspective on the scientific method in sociology, read “The Elements of Scientific Method in Sociology” by F. Stuart Chapin (1914) in the American Journal of Sociology : http://openstaxcollege.org/l/Method-in-Sociology

2.2. Research Methods For information on current real-world sociology experiments, visit: http://openstaxcollege.org/l/Sociology-Experiments

2.3. Ethical Concerns Founded in 1966, the CSA is a nonprofit organization located in Montreal, Quebec, with a membership of 900 researchers, faculty members, students, and practitioners of sociology. Its mission is to promote “research, publication and teaching in Sociology in Canada.” Learn more about this organization at http://www.csa-scs.ca/ .

2.1. Approaches to Sociological Research Merton, Robert. 1968 [1949]. Social Theory and Social Structure . New York: Free Press.

2.2. Research Methods Forget, Evelyn. 2011. “The Town with no Poverty: Using Health Administration Data to Revisit Outcomes of a Canadian Guaranteed Annual Income Field Experiement.” Canadian Public Policy . 37(3): 282-305.

Franke, Richard and James Kaul. 1978. “The Hawthorne Experiments: First Statistical Interpretation.” American Sociological Review 43(5):632–643.

Gilens, Martin. 1996. “Race and Poverty in America: Public Misperceptions and the American News Media.” The Public Opinion Quarterly 60(4):515–541. Grice, Elizabeth. 2006. “Cry of an Enfant Sauvage.” The Telegraph . Retrieved July 20, 2011 ( http://www.telegraph.co.uk/culture/tvandradio/3653890/Cry-of-an-enfant-sauvage.html ).

Haney, C., Banks, W. C., and Zimbardo, P. G. 1973. “Interpersonal Dynamics in a Simulated Prison.” International Journal of Criminology and Penology  1:69–97.

Ivsins, A.K. 2010. “’Got a pipe?’ The social dimensions and functions of crack pipe sharing among crack users in Victoria, BC.” MA thesis, Department of Sociology, University of Victoria. Retrieved February 14, 2014 ( http://dspace.library.uvic.ca:8080/bitstream/handle/1828/3044/Full%20thesis%20Ivsins_CPS.2010_FINAL.pdf?sequence=1 )

Lowrey, Annie. 2013. “Switzerland’s Proposal to Pay People for Being Alive.” The  New York Times Magazine. Retrieved February 17, 2014 ( http://www.nytimes.com/2013/11/17/magazine/switzerlands-proposal-to-pay-people-for-being-alive.html?pagewanted=1&_r=2 ).

Lynd, Robert S. and Helen Merrell Lynd. 1959. Middletown: A Study in Modern American Culture . San Diego, CA: Harcourt Brace Javanovich.

Lynd, Staughton. 2005. “Making Middleton.” Indiana Magazine of History 101(3):226–238.

Marshall, B.D.L., M.J. Milloy,  E. Wood, J.S.G.  Montaner,  and T. Kerr. 2011. “Reduction in overdose mortality after the opening of North America’s first medically supervised safer injecting facility: A retrospective population-based study.” Lancet  377(9775):1429–1437.

Rothman, Rodney. 2000. “My Fake Job.” The New Yorker , November 27, 120.

Sennett, Richard. 2008. The Craftsman . New Haven, CT: Yale University Press. Retrieved July 18, 2011 ( http://www.richardsennett.com/site/SENN/Templates/General.aspx?pageid=40 ).

Smith, Dorothy. 1990. “Textually Mediated Social Organization” Pp. 209–234 in Texts, Facts and Femininity: Exploring the Relations of Ruling. London: Routledge.

Smith, Dorothy. 2005. Institutional Ethnography: A Sociology for People. Toronto: Altamira Press.

Sonnenfeld, Jeffery A. 1985. “Shedding Light on the Hawthorne Studies.” Journal of Occupational Behavior 6:125.

Wood, E., M.W. Tyndall, J.S. Montaner, and T. Kerr. 2006. “Summary of findings from the evaluation of a pilot medically supervised safer injecting facility.” Canadian Medical Association Journal  175(11):1399–1404.

2.3. Ethical Concerns Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, and Social Sciences and Humanities Research Council of Canada. 2010.  Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans . Retrieved February 15, 2014 ( http://www.pre.ethics.gc.ca/pdf/eng/tcps2/TCPS_2_FINAL_Web.pdf ).

Canadian Sociological Association. 2012. Statement of Professional Ethics . Retrieved February 15, 2014 ( http://www.csa-scs.ca/files/www/csa/documents/codeofethics/2012Ethics.pdf ).

Habermas, Jürgen. 1972. Knowledge and Human Interests. Boston: Beacon Press

Weber, Max. 1949. Methodology of the Social Sciences . Translated by H. Shils and E. Finch. Glencoe, IL: Free Press.

Solutions to Section Quiz

1. C | 2. C | 3. D | 4. C | 5. B | 6. C | 7. D | 8. C | 9. A | 10. A | 11. B | 12. A | 13. B | 14. D | 15. A

Image Attributions

Figure 2.3.  Didn’t they abolish the mandatory census? Then what’s this? by  Khosrow Ebrahimpour ( https://www.flickr.com/photos/xosrow/5685345306/in/photolist-9EoT5W-ow4tdu-oeGG4m-oeMEcK-oy2jM2-ovJC8w-oePSRQ-9J2V24-of1Hnu-of243u-of2K2B-of2FHn-owiBSA-owtQN3-of1Ktd-oitLSC-oeVJte-oep8KX-ovEz8w-oeohhF-oew5Xb-oewdWN-owavju-oeMEnV-oweLcN-ovEPGG-ovAQUX-oeo2eL-oeo3Fd-oeoqxh-oxCKnv-ovEzA5-oewFHa-ovHRSz-ow8QtY-oeQY6Y-oeZReR-oeQmHw-oeKXid-oeQLKa-oy6fNT-ow4sVT-oeQMQq-oeQPPr-oeQYbL-ow8hS1-ow4n8v-owiPKS-oeQF41-oeiH5z ) used under CC BY 2.0 ( https://creativecommons.org/licenses/by/2.0/ )

Figure 2.4. Dauphin Canadian Northern Railway Station by Bobak Ha’Eri ( http://commons.wikimedia.org/wiki/File:2009-0520-TrainStation-Dauphin.jpg ) used under CC BY 3.0 license ( http://creativecommons.org/licenses/by/3.0/deed.en )

Figure 2.5.  Punk Band by Patrick ( https://www.flickr.com/photos/lordkhan/181561343/in/photostream/ ) used under CC BY 2.0 ( https://creativecommons.org/licenses/by/2.0/ )

Figure 2.6.  Crack Cocaine Smokers in Vancouver Alleyway ( http://commons.wikimedia.org/wiki/File:Crack_Cocaine_Smokers_in_Vancouver_Alleyway.jpg ) is in the public domain ( http://en.wikipedia.org/wiki/Public_domain )

Figure 2.8.  Muncie, Indiana High School: 1917 by Don O’Brien ( https://www.flickr.com/photos/dok1/3694125269/ ) used under CC BY 2.0 license ( https://creativecommons.org/licenses/by/2.0/ )

Introduction to Sociology - 1st Canadian Edition Copyright © 2014 by William Little and Ron McGivern is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Model Answers

Q: Discuss the importance and sources of hypothesis in social research.

Question asked in UPSC Sociology 2020 Paper 1. Download our app for last 20 year question with model answers.

Model Answer:

Importance of Hypothesis in Social Research

Hypothesis in social research refers to a tentative statement or assumption about the relationship between two or more variables. It is a testable prediction that serves as a starting point for conducting a study. Hypotheses are important in social research for several reasons:

1. Direction and focus: Hypotheses provide a clear direction and focus for the research. They help researchers identify the variables that need to be studied and the relationships that need to be explored. For example, a hypothesis might state that “ higher levels of education lead to higher income levels. ” This statement provides a clear direction for the researcher to investigate the relationship between education and income.

2. Basis for research design: Hypotheses serve as the foundation for designing a research study. They help researchers choose the appropriate methods, techniques, and tools to collect and analyze data. For instance, if a researcher wants to test the hypothesis that “ participation in sports reduces the likelihood of engaging in criminal behavior, ” they might design a study that compares crime rates among individuals who participate in sports and those who do not.

3. Testability: Hypotheses are testable statements that can be either supported or refuted by empirical evidence. This testability is crucial for the scientific process, as it allows researchers to build on existing knowledge and contribute to the understanding of social phenomena. For example, if a hypothesis states that “ social media use increases feelings of loneliness, ” researchers can collect data on social media usage and loneliness levels to test this assumption.

4. Explanation and prediction: Hypotheses help researchers explain and predict social phenomena. By identifying relationships between variables, hypotheses can provide insights into the underlying mechanisms and processes that drive social behavior. For instance, a hypothesis that “ unemployment leads to increased crime rates, ” might suggest that addressing unemployment could help reduce crime.

Sources of hypothesis in social research:

1. Theory: Hypotheses can be derived from existing theories in the field. Theories provide a framework for understanding social phenomena and can suggest relationships between variables that can be tested through research. For example, social learning theory might suggest the hypothesis that “ children who witness violence in their homes are more likely to exhibit aggressive behavior. “

2. Previous research: Hypotheses can be based on the findings of previous studies. Researchers can build on existing knowledge by testing new relationships or exploring the same relationships in different contexts. For example, if a previous study found a relationship between poverty and crime in urban areas, a researcher might hypothesize that the same relationship exists in rural areas.

3. Observations and personal experiences: Researchers can develop hypotheses based on their own observations and experiences. These insights can provide a starting point for investigating social phenomena. For example, a researcher who notices a high rate of teenage pregnancy in their community might hypothesize that a lack of access to sexual education is a contributing factor.

4. Expert opinions and literature reviews: Consulting experts in the field and reviewing existing literature can help researchers identify gaps in knowledge and generate hypotheses. For instance, a review of research on the effects of social media on mental health might reveal conflicting findings, leading a researcher to hypothesize that certain factors, such as the type of social media platform or the amount of time spent online, might moderate these effects.

In conclusion, hypotheses play a crucial role in social research by providing direction, focus, and a basis for research design. They are derived from various sources, including theory, previous research, observations, and expert opinions. By testing hypotheses, researchers can contribute to the understanding of social phenomena and inform policies and interventions aimed at addressing social issues.

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Deductive Approach (Deductive Reasoning)

A deductive approach is concerned with “developing a hypothesis (or hypotheses) based on existing theory, and then designing a research strategy to test the hypothesis” [1]

It has been stated that “deductive means reasoning from the particular to the general. If a causal relationship or link seems to be implied by a particular theory or case example, it might be true in many cases. A deductive design might test to see if this relationship or link did obtain on more general circumstances” [2] .

Deductive approach can be explained by the means of hypotheses, which can be derived from the propositions of the theory. In other words, deductive approach is concerned with deducting conclusions from premises or propositions.

Deduction begins with an expected pattern “that is tested against observations, whereas induction begins with observations and seeks to find a pattern within them” [3] .

Advantages of Deductive Approach

Deductive approach offers the following advantages:

• Possibility to explain causal relationships between concepts and variables
• Possibility to measure concepts quantitatively
• Possibility to generalize research findings to a certain extent

Alternative to deductive approach is  inductive approach.  The table below guides the choice of specific approach depending on circumstances:

Choice between deductive and inductive approaches

Deductive research approach explores a known theory or phenomenon and tests if that theory is valid in given circumstances. It has been noted that “the deductive approach follows the path of logic most closely. The reasoning starts with a theory and leads to a new hypothesis. This hypothesis is put to the test by confronting it with observations that either lead to a confirmation or a rejection of the hypothesis” [4] .

Moreover, deductive reasoning can be explained as “reasoning from the general to the particular” [5] , whereas inductive reasoning is the opposite. In other words, deductive approach involves formulation of hypotheses and their subjection to testing during the research process, while inductive studies do not deal with hypotheses in any ways.

Application of Deductive Approach (Deductive Reasoning) in Business Research

In studies with deductive approach, the researcher formulates a set of hypotheses at the start of the research. Then, relevant research methods are chosen and applied to test the hypotheses to prove them right or wrong.

Generally, studies using deductive approach follow the following stages:

• Deducing  hypothesis from theory.
• Formulating  hypothesis in operational terms and proposing relationships between two specific variables
• Testing  hypothesis with the application of relevant method(s). These are quantitative methods such as regression and correlation analysis, mean, mode and median and others.
• Examining  the outcome of the test, and thus confirming or rejecting the theory. When analysing the outcome of tests, it is important to compare research findings with the literature review findings.
• Modifying  theory in instances when hypothesis is not confirmed.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research approaches. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,   research design ,  methods of data collection ,   data analysis  and   sampling   are explained in this e-book in simple words.

John Dudovskiy

[1] Wilson, J. (2010) “Essentials of Business Research: A Guide to Doing Your Research Project” SAGE Publications, p.7

[2] Gulati, PM, 2009, Research Management: Fundamental and Applied Research, Global India Publications, p.42

[3] Babbie, E. R. (2010) “The Practice of Social Research” Cengage Learning, p.52

[4] Snieder, R. & Larner, K. (2009) “The Art of Being a Scientist: A Guide for Graduate Students and their Mentors”, Cambridge University Press, p.16

[5] Pelissier, R. (2008) “Business Research Made Easy” Juta & Co., p.3

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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Social Surveys – Strengths and Limitations

Table of Contents

Last Updated on November 2, 2023 by Karl Thompson

Social Surveys are a quantitative, positivist research method consisting of structured questionnaires and interviews. This post considers the theoretical, practical and ethical advantages and disadvantages of using social surveys in social research. 

The strengths and limitations below are mainly based around surveys administered as self-completion questionnaires.

Theoretical Factors

Theoretical strengths of social surveys

Detachment, objectivity and validity.

Positivists favour questionnaires because they are a detached and objective (unbiased) method, where the sociologist’s personal involvement with respondents is kept to a minimum.

Hypothesis Testing

Questionnaires are particularly useful for testing hypotheses about cause and effect relationships between different variables, because the fact that they are quantifiable allows us to find correlations.

For example, based on government statistics on educational achievement we know that white boys on Free School Meals achieve at a significantly lower level than Chinese girls on Free School Meals. We reasonably hypothesise that this is because differences in parental attitudes – Chinese parents may value education more highly, and they may be stricter with their children when it comes to homework compared to white parents. Good questionnaire design and appropriate sampling would enable us to test out this hypothesis. Good sampling would further allow us to see if those white working class boys who do well have parents with similar attitudes to those Chinese girls who do well.

Representativeness

Questionnaires allow the researcher to collect information from a large number of people, so the results should be more representative of the wider population than with more qualitative methods. However, this all depends on appropriate sampling techniques being used and the researchers having knowledge of how actually completes the questionnaire.

Reliability

When the research is repeated, it is easy to use the exact same questionnaire meaning the respondents are asked the exact same questions in the same order and they have the same choice of answers.

Theoretical Limitations

Issues affecting validity – Interpretivists make a number of criticisms of questionnaires .

The Imposition Problem

Misinterpetation of questions.

Interpretivists argue that the detached nature of questionnaires and the lack of close contact between researcher and respondent means that there is no way to guarantee that the respondents are interpreting the questions in the same way as the researcher. This is especially true where very complex topics are involved – If I tick ‘yes’ that I am Christian’ – this could mean a range of things – from my being baptised but not practising or really believing to being a devout Fundamentalist. For this reason Interpretivists typically prefer qualitative methods where researchers are present to clarify meanings and probe deeper.

Researchers may not be present to check whether respondents are giving s ocially desirable answers , or simply lying, or even to check who is actually completing the questionnaire. At least with interviews researchers are present to check up on these problems (by observing body language or probing further for example).

Issues affecting representativeness

Practical factors.

Practical Strengths of Social Surveys

Questionnaires are a quick and cheap means of gathering large amounts of data from large numbers of people, even if they are widely dispersed geographically if the questionnaire is sent by post or conducted online. It is difficult to see how any other research method could provide 10s of millions of responses as is the case with the UK national census.

The data is quick to analyse once it has been collected. With online questionnaires, pre-coded questions can be updated live.

Practical Limitations

Structured Interviews are also considerably more expensive than self-completion questionnaires.

Ethical Factors

Ethical strengths of surveys

Ethical limitations.

They are best avoided when researching sensitive topics.

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2.2 Research Methods

Learning objectives.

By the end of this section, you should be able to:

• Recall the 6 Steps of the Scientific Method
• Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysis.
• Explain the appropriateness of specific research approaches for specific topics.

Sociologists examine the social world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study. Planning the research design is a key step in any sociological study. Sociologists generally choose from widely used methods of social investigation: primary source data collection such as survey, participant observation, ethnography, case study, unobtrusive observations, experiment, and secondary data analysis , or use of existing sources. Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use. When you are conducting research think about the best way to gather or obtain knowledge about your topic, think of yourself as an architect. An architect needs a blueprint to build a house, as a sociologist your blueprint is your research design including your data collection method.

When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher wouldn’t stroll into a crime-ridden neighborhood at midnight, calling out, “Any gang members around?”

Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviors, early education, or the Ku Klux Klan. Researchers can’t just stroll into prisons, kindergarten classrooms, or Klan meetings and unobtrusively observe behaviors or attract attention. In situations like these, other methods are needed. Researchers choose methods that best suit their study topics, protect research participants or subjects, and that fit with their overall approaches to research.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviors and opinions, often in the form of a questionnaire or an interview. The survey is one of the most widely used scientific research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

At some point, most people in the United States respond to some type of survey. The 2020 U.S. Census is an excellent example of a large-scale survey intended to gather sociological data. Since 1790, United States has conducted a survey consisting of six questions to received demographical data pertaining to residents. The questions pertain to the demographics of the residents who live in the United States. Currently, the Census is received by residents in the United Stated and five territories and consists of 12 questions.

Not all surveys are considered sociological research, however, and many surveys people commonly encounter focus on identifying marketing needs and strategies rather than testing a hypothesis or contributing to social science knowledge. Questions such as, “How many hot dogs do you eat in a month?” or “Were the staff helpful?” are not usually designed as scientific research. The Nielsen Ratings determine the popularity of television programming through scientific market research. However, polls conducted by television programs such as American Idol or So You Think You Can Dance cannot be generalized, because they are administered to an unrepresentative population, a specific show’s audience. You might receive polls through your cell phones or emails, from grocery stores, restaurants, and retail stores. They often provide you incentives for completing the survey.

Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel, think, and act—or at least how they say they feel, think, and act. Surveys can track preferences for presidential candidates or reported individual behaviors (such as sleeping, driving, or texting habits) or information such as employment status, income, and education levels.

A survey targets a specific population , people who are the focus of a study, such as college athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes. Most researchers choose to survey a small sector of the population, or a sample , a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. As a result, a Gallup Poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people.

After selecting subjects, the researcher develops a specific plan to ask questions and record responses. It is important to inform subjects of the nature and purpose of the survey up front. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument, which is a means of gathering the information.

A common instrument is a questionnaire. Subjects often answer a series of closed-ended questions . The researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question. This kind of questionnaire collects quantitative data —data in numerical form that can be counted and statistically analyzed. Just count up the number of “yes” and “no” responses or correct answers, and chart them into percentages.

Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” or checkbox options. These types of inquiries use open-ended questions that require short essay responses. Participants willing to take the time to write those answers might convey personal religious beliefs, political views, goals, or morals. The answers are subjective and vary from person to person. How do you plan to use your college education?

Some topics that investigate internal thought processes are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of personal explanation is qualitative data —conveyed through words. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of in-depth material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and it is a way of conducting surveys on a topic. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly.

Questions such as “How does society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. The researcher will also benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Surveys often collect both quantitative and qualitative data. For example, a researcher interviewing people who are incarcerated might receive quantitative data, such as demographics – race, age, sex, that can be analyzed statistically. For example, the researcher might discover that 20 percent of incarcerated people are above the age of 50. The researcher might also collect qualitative data, such as why people take advantage of educational opportunities during their sentence and other explanatory information.

The survey can be carried out online, over the phone, by mail, or face-to-face. When researchers collect data outside a laboratory, library, or workplace setting, they are conducting field research, which is our next topic.

Field Research

The work of sociology rarely happens in limited, confined spaces. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people think and behave. It seeks to understand why they behave that way. However, researchers may struggle to narrow down cause and effect when there are so many variables floating around in a natural environment. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables. Indeed, much of the data gathered in sociology do not identify a cause and effect but a correlation .

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

Sociologists have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and fingers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see whether anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a writer, or a sociologist, will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. A researcher might work as a waitress in a diner, experience homelessness for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in analyzing data and generating results.

In a study of small towns in the United States conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in U.S. towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised the purpose of their study.

This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd & Lynd, 1929).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behavior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job.

Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book and describe what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study . To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

The book she wrote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms.

Ethnography

Ethnography is the immersion of the researcher in the natural setting of an entire social community to observe and experience their everyday life and culture. The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a social group.

An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record data, and collate the material into results.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith (1990), institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male- dominated societies and power structures. Smith’s work is seen to challenge sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from the male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada n.d.). Smith’s three major works explored what she called “the conceptual practices of power” and are still considered seminal works in feminist theory and ethnography (Fensternmaker n.d.).

Sociological Research

The making of middletown: a study in modern u.s. culture.

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000) as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minorities or outsiders—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds objectively described what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. As a result, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that Muncie was divided into business and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was an emerging material reality of the 1920s.

As the Lynds worked, they divided their manuscript into six chapters: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

Middletown: A Study in Modern American Culture was not only published in 1929 but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (Caplow, Hicks, & Wattenberg. 2000).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times. Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data was important—and interesting—to the U.S. public.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that while offering depth on a topic, it does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can contribute tremendous insight. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” growth and nurturing. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be obtained by any other method.

Experiments

You have probably tested some of your own personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis.

One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach.

There are two main types of experiments: lab-based experiments and natural or field experiments. In a lab setting, the research can be controlled so that more data can be recorded in a limited amount of time. In a natural or field- based experiment, the time it takes to gather the data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher.

As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens (cause), then another particular thing will result (effect). To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables.

Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group. The experimental group is exposed to the independent variable(s) and the control group is not. To test the benefits of tutoring, for example, the sociologist might provide tutoring to the experimental group of students but not to the control group. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record of a student, for example.

And if a researcher told the students they would be observed as part of a study on measuring the effectiveness of tutoring, the students might not behave naturally. This is called the Hawthorne effect —which occurs when people change their behavior because they know they are being watched as part of a study. The Hawthorne effect is unavoidable in some research studies because sociologists have to make the purpose of the study known. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985).

A real-life example will help illustrate the process. In 1971, Frances Heussenstamm, a sociology professor at California State University at Los Angeles, had a theory about police prejudice. To test her theory, she conducted research. She chose fifteen students from three ethnic backgrounds: Black, White, and Hispanic. She chose students who routinely drove to and from campus along Los Angeles freeway routes, and who had had perfect driving records for longer than a year.

Next, she placed a Black Panther bumper sticker on each car. That sticker, a representation of a social value, was the independent variable. In the 1970s, the Black Panthers were a revolutionary group actively fighting racism. Heussenstamm asked the students to follow their normal driving patterns. She wanted to see whether seeming support for the Black Panthers would change how these good drivers were treated by the police patrolling the highways. The dependent variable would be the number of traffic stops/citations.

The first arrest, for an incorrect lane change, was made two hours after the experiment began. One participant was pulled over three times in three days. He quit the study. After seventeen days, the fifteen drivers had collected a total of thirty-three traffic citations. The research was halted. The funding to pay traffic fines had run out, and so had the enthusiasm of the participants (Heussenstamm, 1971).

Secondary Data Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data analysis . Secondary data does not result from firsthand research collected from primary sources, but are the already completed work of other researchers or data collected by an agency or organization. Sociologists might study works written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines, or organizational data from any period in history.

Using available information not only saves time and money but can also add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behavior and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or social media.

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like the U.S. Bureau of Labor Statistics or the World Health Organization (WHO), publish studies with findings that are useful to sociologists. A public statistic like the foreclosure rate might be useful for studying the effects of a recession. A racial demographic profile might be compared with data on education funding to examine the resources accessible by different groups.

One of the advantages of secondary data like old movies or WHO statistics is that it is nonreactive research (or unobtrusive research), meaning that it does not involve direct contact with subjects and will not alter or influence people’s behaviors. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process.

Using available data does have its challenges. Public records are not always easy to access. A researcher will need to do some legwork to track them down and gain access to records. To guide the search through a vast library of materials and avoid wasting time reading unrelated sources, sociologists employ content analysis , applying a systematic approach to record and value information gleaned from secondary data as they relate to the study at hand.

Also, in some cases, there is no way to verify the accuracy of existing data. It is easy to count how many drunk drivers, for example, are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not survey the topic from the precise angle the researcher seeks. For example, the average salaries paid to professors at a public school is public record. But these figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they’ve been teaching.

When conducting content analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, when Robert S. Lynd and Helen Merrell Lynd gathered research in the 1920s, attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal insights about small U.S. communities. Today, it is an illustration of 1920s attitudes and values.

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6 Advantages of Hypothesis in Social Research

Hypotheses are of different types and kinds and it is not easy to develop a good hypothesis. But a question arises as to what is its utility in social research. There is not one but many advantages of hypothesis in social research. These are:

1. It is with the help of hypothesis, that it becomes easy to decide as to what type of data is to be collected and what type of data is simply to be ignored.

2. Hypothesis makes it clear as what is to be accepted, proved or disproved and that what is the main focus of study.

3. It helps the investigator in knowing the direction in which he is to move. Without hypothesis it will be just duping in the dark and not moving in the right direction.

Image Source : cep-probation.org

4. A clear idea about hypothesis means saving of time money and energy which otherwise will be wasted, thereby botheration of trial and error will be saved.

5. It helps in concentrating only on relevant factors and dropping irrelevant ones. Many irrelevant factors which otherwise get into the study can easily be ignored.

6. A properly formulate hypothesis is always essential for drawing proper and reason­able conclusions.

Hypothesis in brief, is the pivot of the whole study. Without well formulated hypothesis the whole study will be out of focus and it will be difficult to drawn rights and proper conclusions. In fact, hypothesis is a necessary link between theory and investigation which will result in the addition of existing knowledge.

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6 Advantages of Hypothesis in Social Research

Hypotheses are of different types and kinds and it is not easy to develop a good hypothesis. But a question arises as to what is its utility in social research. There is not one but many advantages of hypothesis in social research. These are:

1. It is with the help of hypothesis, that it becomes easy to decide as to what type of data is to be collected and what type of data is simply to be ignored.

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2. Hypothesis makes it clear as what is to be accepted, proved or disproved and that what is the main focus of study.

3. It helps the investigator in knowing the direction in which he is to move. Without hypothesis it will be just duping in the dark and not moving in the right direction.

Image Source : cep-probation.org

4. A clear idea about hypothesis means saving of time money and energy which otherwise will be wasted, thereby botheration of trial and error will be saved.

5. It helps in concentrating only on relevant factors and dropping irrelevant ones. Many irrelevant factors which otherwise get into the study can easily be ignored.

6. A properly formulate hypothesis is always essential for drawing proper and reason­able conclusions.

Hypothesis in brief, is the pivot of the whole study. Without well formulated hypothesis the whole study will be out of focus and it will be difficult to drawn rights and proper conclusions. In fact, hypothesis is a necessary link between theory and investigation which will result in the addition of existing knowledge.

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Ambidextrous leadership: an emphasis on the mediating role of knowledge sharing and knowledge search

• Original Research
• Published: 19 June 2024

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• Ata Harandi   ORCID: orcid.org/0000-0002-4536-8947 1 ,
• Payvand Mirzaeian Khamseh   ORCID: orcid.org/0000-0003-0767-4481 2 &
• Shib Sankar Sana   ORCID: orcid.org/0000-0002-7834-8969 3  

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Innovation is widely being recognized as a crucial determinant of organizations’ competitive advantage. This study delves into ambidextrous leadership, encompassing two seemingly contrasting yet potentially complementary behaviors—opening and closing leadership. The aim is to elucidate how a leader can pave the way for achieving innovation among employees, and throughout the entire organization by leveraging the dual strategies of knowledge sharing and knowledge search. This research is descriptive in nature, grounded in a positivist research philosophy with an applied research orientation. The proposed research strategy involves a survey employing quantitative methods. Ambidextrous leadership characterized by both opening and closing approaches has the potential to enhance employees’ innovation through knowledge sharing. Furthermore, the proposed study reveals that ambidextrous leadership encompassing Transactional and Transformational leadership styles fosters organizational innovation through knowledge search. As social information processing technology is being updated continuously, leaders’ demonstration on both the opening and closing behaviors can drive innovation at both employee and organizational levels. Moreover, the mediating roles of knowledge sharing and knowledge seeking are vital to achieve these outcomes. However, the eighth hypothesis which explores the moderating influence of strategic flexibility does not yield significant results. A balanced strategy between these dual roles is more innovative and adaptive organizational culture.

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Ata Harandi

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Project 2025 leader The Heritage Foundation calls for Social Security cuts

Heritage's recommendation to raise the retirement age to 70 would amount to a 20% cut in benefits

Written by Zachary Pleat

Published 06/18/24 12:54 PM EDT

The Heritage Foundation, which has played a central role in organizing the planned extremist takeover of the federal government known as Project 2025 for the next Republican president, is now calling for the Social Security retirement age to be raised to 70. Heritage fearmongered about a possible future benefit cut in order to argue for cutting benefits now.

On May 6, the Social Security board of trustees released their annual report outlining the short- and long-term financial projections of the Social Security insurance programs serving retirees, survivors of deceased workers, and people with disabilities. This year, the report actually noted that the program’s long-term financial outlook had improved somewhat over the past year. According to the report, with no changes to current law, the retirement trust fund will continue to be able to pay out full benefits until 2033, at which time the trust fund will become depleted and would require an across-the-board benefit cut of 21% in order to reflect the amount of Social Security revenue still coming in.

A June 17 Heritage Foundation post used this possible future benefit cut to demand that more immediate cuts to benefits be made by raising the retirement age and changing the program’s inflation adjustment:

If Congress does nothing to address Social Security’s shortfalls, benefits will be cut by 21 percent, across the board beginning in just nine years—in 2033. That means that anyone who is of Generation X or younger will not receive a single full benefit. Even Baby Boomers and Silent Generation retirees will be subject to cuts. … To restore Social Security’s intent, policymakers should gradually increase the normal retirement age from 67 to 69 or 70—moving the age up by one or two months per year—and index it to life expectancy. … While updating Social Security’s retirement age is an important component of reform, it would only solve about 20 percent to 30 percent of the program’s shortfalls. A more accurate inflation adjustment would solve another 20 percent to 25 percent of the program’s shortfalls.

Heritage also waxed poetic about the virtues of people spending longer in the workforce, with Roe Institute senior research fellow Rachel Greszler arguing that “older workers’ wisdom and experience provides an invaluable insight and mentorship to younger workers.”

However, as the Center on Budget and Policy Priorities explained prior to the release of this year’s trustee report, raising the Social Security retirement age would have the effect of cutting benefits by about the same amount as the projected 2033 benefit cut under current law:

The irony of that argument is that over time, raising the retirement age would yield the same result that they purport to want to avoid — a large, across-the-board benefit cut. Raising the retirement age to 70 would ultimately cut average lifetime benefits for new retirees by nearly 20 percent, whereas if Social Security’s reserves are depleted, congressional inaction would force a 23 percent cut for all beneficiaries.

Calls by The Heritage Foundation to reduce Social Security benefits should raise alarm bells. Heritage is not just some right-wing think tank; it is the driving force behind Project 2025 , which aims to radically change the federal government in numerous regressive ways should former President Donald Trump win his reelection bid in November:

The Heritage Foundation’s nearly 900-page policy book, titled Mandate for Leadership: A Conservative Promise , describes Project 2025’s priorities and how they would be implemented, broken down by departments in the federal bureaucracy and organized around “four pillars that will, collectively, pave the way for an effective conservative administration: a policy agenda, personnel, training, and a 180-day playbook.” Written primarily by former Trump officials and conservative commentators connected to The Heritage Foundation, these proposals would severely inhibit the federal government’s protections around reproductive rights, LGBTQ and civil rights, climate change efforts, and immigration.

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• Published: 24 June 2024

The impact of digital transformation on the quality and safety level of agricultural exports: evidence from Chinese listed companies

• Yuchen Liu   ORCID: orcid.org/0009-0000-2336-8092 1 ,
• Yinguo Dong 1 &
• Weiwen Qian 2  

Humanities and Social Sciences Communications volume  11 , Article number:  817 ( 2024 ) Cite this article

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Enhancing the quality and safety of exported agricultural products and improving export competitiveness is the key to establishing enhanced competitive advantages in agricultural products, developing a trade powerhouse and realising high-quality development of agriculture. This paper uses the data of Chinese listed companies and Chinese Customs from 2007 to 2016 to discuss the effect and mechanism of digital transformation of enterprises on the quality and safety level of export agricultural products by using the staggered differential method. The study shows that (1) Enterprise digital transformation effectively improves the quality and safety of exported agricultural products, and this result holds after endogeneity, placebo and multiple robustness tests; (2) Heterogeneity analyses reveal that the quality and safety effect of enterprise digital transformation is greater for exporting to developed countries’ markets, non-state-owned enterprises and enterprises in the eastern region, in addition to bulk agricultural products and consumer-oriented agricultural products; (3) Mechanism analyses shows that enterprise digital transformation raises the quality and safety of exported agricultural products through technological innovation, product tracing, information sharing and quality assurance effects.

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Introduction.

To comprehensively improve the level of product quality and promote the development of a strong trading nation, in 2023, the Chinese State Council issued the Outline for the Construction of a Strong Quality Country, declaring the need for China to shift from promoting development to improving quality and efficiency, raising the quality of exported products and the value of exported units and realising product quality upgrading Footnote 1 . Agriculture is the foundation of the country, quality safety is the basis of improving the quality of agricultural products, and vigorously improving agricultural products’ quality is essential for advancing the country’s high-quality development. China has become more involved in raising the incomes of the country’s 600 million farmers. However, issues related to sanitary and phytosanitary (SPS) measures which are frequently encountered in exports such as excessive pesticide residues, preservatives, microbial contamination and metallic foreign objects have become critical factors hindering Chinese agricultural product exports; thus, agricultural products are caught in the quality upgrading dilemma (Liu and Dong 2021 ). Statistics released by the Korea Food and Drug Administration on irregularities in food products imported in South Korea show that 75 cases arose from Chinese agri-food products from April to June 2023, indicating a 23% increase compared with the same period in 2022 Footnote 2 . In the first half of 2023, the US Food and Drug Administration identified 975 batches of products from China, of which 418 cases were agri-food products, representing an annual increase of 13.9%, accounting for 42.87% of the notified products from China Footnote 3 . Consequently, to promote the high-quality development of China’s agricultural trade, it is essential to transition exported agricultural products from quantity to quality and urgently improve the quality and safety of Chinese exported agricultural products. In November 2021, the Ministry of Commerce issued the 14th Five-Year Plan for the High-Quality Development of Foreign Trade, which listed ‘digital trade’ as a key project of foreign trade and proposed to advance digital empowerment, accelerate digital transformation, promote the in-depth fusion of digital technology and trade development and continuously strengthen the engine of foreign trade development Footnote 4 . Based on the digital transformation of promote the development of export trade, is undoubtedly the important way to realising the strategic goal of trade power.

Enterprise digitalisation refers to the process of enterprises’ industrial upgrading and transformation using emerging technologies (Zhang et al. 2021 ). As the main body of the agricultural industry chain, digital transformation has altered the value creation and value capture of the agricultural industry chain and transformed the agricultural enterprise model (Yi et al. 2021 ). Central Document No. 1 of 2023 noted that the implementation of in-depth digital village development should be conducted, promoting research and development (R&D) and digital application scenarios Footnote 5 . The Party Central Committee clearly attaches considerable importance to digital economy development as an important aspect of high-quality agricultural development through the deep integration of the digital economy in rural industry for prosperity, agricultural modernisation and development to provide inexhaustible water and sustained momentum. The scale of the agricultural digital economy is expected to reach 1.26 trillion yuan by 2025, 7.8 trillion yuan by 2035 and 24 trillion yuan by 2050 Footnote 6 . From a theoretical perspective, enterprises’ digital transformation can improve the control and supervision of production processes through intelligent monitoring and optimising the digital management of the supply chain to improve the standardisation and safety of agricultural production (Wu and Yao 2023 ). Furthermore, digitalising data and information can improve information transmission and sharing efficiency to promote the continuous exchange of information between enterprises and consumers and improve the safety and credibility of agricultural products (Li et al. 2023 ). As primary participants in market economic activities and the source of vitality for economic and social development, can agricultural enterprises improve the quality and safety of exported products and address the quality and safety problems associated with exported agricultural products through digital transformation? If so, what are the mechanisms of action? Is there heterogeneity in the impact of digital transformation among agricultural enterprises? It is of great theoretical value and practical significance to answer the above questions from the micro level. In theory, it enriches and expands the theoretical framework of the impact of digital transformation on the export of enterprises, and lays the foundation for the high-quality development of China’s foreign trade by evaluating the digitalisation of enterprises. At the same time, it helps to deeply understand the new driving mechanism for upgrading the quality and safety level of enterprises’ export agricultural products in the digital era, and also provides strong empirical evidence for helping Chinese foreign trade enterprises to ride the ‘digital revolution’ and achieve high-quality export development.

The remainder of this paper is structured as follows. Section “Literature review” presents the literature review. Sections “Theoretical research and hypothesis formulation” conducts theoretical and mechanism analyses. Section “Model setting, variable construction and data sources” describes the empirical model, data and estimation strategy. Section “Empirical results and analysis” details the results. Section “Mechanism of action test” presents the mechanism testing and Section “Conclusions and policy implications” concludes.

Literature review

Three strands of literature are closely related to this study. The first strand of literature has examined the quality of agricultural exports. Studies on product quality were first proposed by Linder ( 1961 ), who argued that the level of per capita income has a direct impact on trade development and that income levels are highly correlated with national product quality requirements. Melitz ( 2003 ) argued against the assumption of homogeneity among production enterprises, proposing a novel trade theory, and research began to really consider the heterogeneity of enterprises’ product quality. Regarding the measurement of agricultural product quality, the most common models have included unit value (Schott 2004 ), ex-post backcasting (Khandelwal et al. 2013 ; Shi 2014 ) and nested logit (Dong and Huang 2016 ) methods. Regarding the influencing factors of the quality of exported agricultural products, some studies have found that trade measures such as the positive list system (Chen and Xu 2017 ), SPS measures in importing countries (Dong and Liu 2019 ), maximum residue limitation standards (Jiang and Yao 2019 ) and the development of digital finance (Li and Wang 2024 ) have compelled the quality upgrade of China’s exported agricultural products.

The second category is research on enterprise digitalisation, which focuses on its connotations, measurement and related economic effects. Studies have found that digital transformation refers to the comprehensive transformation and optimisation of business models, operating processes, value creation and delivery methods by organisations or enterprises using digital technologies and information technology to enhance their competitiveness, innovation and sustainable development (Vial, 2019 ; Verhoef et al. 2019 ). Digitalisation measurement has included three aspects of investment, application and business transformation, using annual reports of Chinese listed companies in different sample years and examining machine learning word frequency statistics to measure the digital transformation of Chinese enterprises (Liu, 2020 ; Du et al. 2022 ). In terms of the economic effects of digitisation, scholars have focused on the impact of digitalisation on enterprises’ total factor productivity, innovation, international trade, input-output efficiency and specialised division of labour. For example, Zhao et al. ( 2021 ) studied that digital transformation can promote total factor productivity by improving innovation capacity, optimising human capital structure, and reducing costs. Chaney ( 2014 ) suggested that the widespread use of information technology (ICT) can promote export growth by reducing information search and distribution costs. Loebbecke and Picot ( 2015 ) found that digital transformation can reduce the cost of effective information acquisition, optimise the enterprise’s R&D model and improve the efficiency of innovation investment. Yuan et al. ( 2021 ) argued that the digital transformation of enterprises has significantly improved the specialisation level of listed enterprises in China. Liu et al. ( 2021 ) found that there is a inverted U-shaped relationship between enterprise digital investment and efficiency.

The third strand of literature has examined the impact of firms’ digital transformation on export trade. Freund and Weinhold ( 2004 ) first suggested that adopting digital technologies removes information barriers between trading parties, widening the network of trade links between countries and expanding trade flow and scope. It has also been argued that the digital skill factor is increasingly replacing the labour factor as the main driver of firms’ production and exports (Acemoglu and Restrepo 2020 ). Qi and Cai ( 2020 ) found that digital transformation can expand exports, reduce entry costs and increase the number of exports, product variety and trading partners. Meijers ( 2014 ) argued that firms’ digital innovation in an industry improves the added value of products and facilitates advancement to the middle and high end of the global value chain. Nambisan et al. ( 2017 ) found that enterprises can realise rapid iterative upgrading of export products digital transformation, expediently adjust the range of export products and export high-quality products that are adapted to international market demands. For the study of agricultural trade, Liu and Gao ( 2022 ) used a vector auto-regressive model, revealing a stable dynamic relationship between the digital economy and the total number of imported and exported agricultural products. The authors also demonstrated that different agricultural products have different characteristics and the digital economy can explain the total import and export amount of animal products, grains and fruits to a greater extent. Ma and Guo ( 2023 ) found that the digital economy can expand the scale of agricultural exports and increase the technical complexity of agricultural exports.

Examining previous literature reveals that researchers have paid limited attention to the impact of enterprises’ digital transformation on agricultural product exports, and even less research has analysed the impact mechanism of digital transformation on the quality and safety of exported agricultural products according to the characteristics of agricultural products. Although Du et al. ( 2022 ) and Hong et al. ( 2022 ) both emphasized the positive impact of digital transformation on the quality of export products of enterprises, they failed to analyse the impact of digital transformation on the quality and safety of export agricultural products from the perspective of quality and safety based on the characteristics of agricultural products. Compared with industrial products, the biggest challenge facing the quality and safety of exported agricultural products is food safety and quality, which refers to ensuring that the whole process of agricultural products, from production, to processing to export, meets the quality and safety standards of importing countries and keeping them fresh and safe during transport, storage and sale. In the context of strongly advocating the empowerment of traditional agriculture with digital technology and comprehensively promoting the digital transformation of agriculture, it is essential to leverage enterprise digital transformation to address the challenges of ensuring food safety and the quality of exported agricultural products. This study uses the data of Chinese listed companies and Chinese Customs data from 2007 to 2016 to examine the of digital transformation intensity of listed companies exporting agricultural products using Python crawler technology and adopts a staggered difference-in-differences (DID) model to explore the effect and mechanism of the influence of enterprise digital transformation on the quality and safety of exported agricultural products.

Compared with the existing literature, the marginal contributions of this study are reflected in the following three aspects. (1) In terms of research perspective, this study constructs an indicator system for the quality and safety level of agricultural products from four dimensions of quality tracing, information communication, quality control, and risk prevention and focuses for the first time on the impact of enterprises’ digital transformation on the quality and safety of exported agricultural products from the perspective of food safety, expanding the research scope of the economic effects of enterprises’ digital transformation and exploring the issue of its intrinsic impact mechanisms, laying the foundation for assessing the impact of enterprises’ digitalisation on the high-quality development of China’s foreign trade. (2) In terms of research content, this paper enriches and expands the theoretical framework of the impact of digital transformation on the export of enterprises, introduces digital transformation into the heterogeneous trade model of enterprises, and discusses the impact and specific mechanism of digital transformation on the quality and safety level of export agricultural products based on the general equilibrium perspective and combined with the characteristics of agricultural products. (3) In terms of research data and modelling methodology, this study combines data from Chinese listed companies with Chinese Customs data, uses crawler technology to quantify the intensity of the digital transformation of listed companies exporting agricultural products in five dimensions: digital technology application, digital information system, digital intelligent management, digital marketing model, digital efficiency enhancement and explores the impact effect of enterprise digital transformation on the quality and safety of exported agricultural products and the mechanisms of impact based on a staggered DID model, providing micro-level evidence regarding enterprises’ digital transformation.

Theoretical research and hypothesis formulation

Theoretical models.

Based on the heterogeneous trade model proposed by Melitz ( 2003 ) and Antoniades ( 2015 ), this paper incorporates the factors of digital transformation into an open economic framework, comprehensively considering the personalised needs of consumers and the cost characteristics of manufacturers. By solving for the maximisation of consumer utility and enterprise profit, the equilibrium of enterprise quality investment is obtained, and the impact of digital transformation on the quality and safety level of exported agricultural products is theoretically discussed.

Assume that firms in the country \(i\) export products to the country \(j\) where \(i,j\in 1,\mathrm{..}.N\) , the country \(j\) has \({L}_{j}\) consumers who consume the product set \({\varOmega }_{j}\) and that the utility function of the consumers is of the Dixit–Stiglitz form, which can be expressed as follows:

In Eq. ( 1 ), \(\sigma > 1\) denotes the elasticity of substitution between different commodities. \({q}_{ij}(\omega )\) is the quality of the product \(\omega\) , and \({x}_{ij}(\omega )\) represents the demand of the country \(j\) for the product \(\omega\) in the country \(i\) , which can be expressed as follows:

Equation ( 2 ) represents the optimal demand of consumers in the country \(j\) for the product \(\omega\) in the country \(i\) . \({p}_{ij}(\omega )\) is the price of the product \(\omega\) , \({P}_{j}(\omega )=\{{{\int }_{\omega \in {\varOmega }_{j}}[{p}_{ij}(\omega )/{q}_{ij}(\omega )]}^{1-\sigma }]d\omega {\}}^{\frac{1-\sigma }{\sigma }}\) is the total price index of all products consumed in the country \(j\) , and \({E}_{j}\) is the total expenditure on these products in the country \(j\) . As the price of a product falls or the quality improves, consumer demand increases.

Enterprises

Assuming that the firm is in a monopolistically competitive market, the firm faces two types of fixed costs, namely fixed export costs \({f}_{ij}\) (excluding trade variable costs \({\tau }_{ij}\) ) and fixed production costs \({f}_{d}{q}_{ij}^{\beta }\) . \({f}_{d}\) denotes the fixed cost of production in the absence of quality adjustment. \(\beta > 0\) denotes a measure of the elasticity of fixed production costs with respect to the quality of the product, which usually consists of fixed capital inputs that include the firm’s R&D or production equipment inputs. Since the digital transformation of a firm reduces the cost of search and the cost of information exchange, \({\tau }_{ij}=\alpha f(\cdot ){e}^{-dig}\) , \({\tau }_{ij}^{\text{'}}=-dig\ast \alpha f(\cdot ){e}^{-dig-1} < 0\) . Assuming that the unit cost of a firm’s quality inputs is \({\mu }_{i}\) , its relationship with digital transformation can be expressed as \({\mu }_{i}(dig)\) , \({\mu }_{i}^{\text{'}}(dig) < 0\) . Here, \({\mu }_{i}\) is also influenced by other factors of production such as labour and capital. Using \(c(\cdot )\) as a measure of the unit cost of other influences on the quality inputs of the firm, the cost of quality due to digital transformation is denoted as: \({\mu }_{i}=\frac{c(\cdot )}{{e}^{dig}}\) .

Define \({\theta }_{L}\) as the productivity of a firm’s labour force, and the relationship between the productivity of a firm’s labour force and the digital transformation of a firm as \({\theta }_{L}(dig)\) , where \({\theta }_{L}^{\text{'}}(dig) > 0\) . Assuming that each labour force has increased its productivity by acquiring better technology ( \(\xi\) ), it follows:

Assuming that quality is positively related to the marginal cost of production, the total factor productivity (TFP) function per unit of firm in the country \(i\) can be defined as:

In the above equation, \(\varphi ({\theta }_{k},{\theta }_{L})\) increases as \({\theta }_{k}\) and \({\theta }_{L}\) increase. Therefore, the marginal cost of production of a product exported from the country \(i\) to the country \(j\) should be \({\mu }_{i}{\tau }_{ij}{q}_{ij}^{\alpha }/\varphi ({\theta }_{k},{\theta }_{L})\) . Where \(\alpha \in (0,1)\) represents the elasticity of marginal cost with respect to product quality.

Balanced quality inputs from enterprises

Combining the consumer utility function and the firm’s production function, the firm’s profit from exports from the country \(i\) to the country \(j\) should be:

For the first order condition, this yields:

From Eqs. ( 6 ) and ( 7 ), the optimal quantity decision made by the firm can be determined by the following conditions:

In Eq. ( 8 ), \(\beta -(1-\alpha )(\sigma -1) > 0\) , holding all other factors constant, the firm’s optimal quality increases as the cost of trade \({\tau }_{ij}\) decreases ( \(1-\alpha < 0\) ). Combined with the conditions of \({\tau }_{ij}^{\text{'}} < 0\) , the hypothesis can be obtained:

Hypothesis 1: Digital transformation of enterprises improves the quality and safety of exported agricultural products.

Enterprise digital transformation raises the quality and safety of exported agricultural products through technological innovation, product tracing, information sharing and quality assurance effects (See Fig. 1 ).

The mechanism of the digital transformation of enterprises and the quality and safety of exported agricultural products.

Technological innovation effect

Innovation is a powerful tool to strengthen enterprises’ competitive advantage and the primary driving force for incentivising enterprises to enter the global market and advance export quality upgrading (Carboni and Medda 2020 ). First, the digital transformation of enterprises brings digital production factors to agricultural trade. Digital production factors such as the Internet of Things, big data analytics and artificial intelligence have improved the efficiency of agricultural production and management, increased the transparency of the market, and promoted cross-border cooperation and innovation, creating good conditions for the sustainable development of agricultural trade and the income growth of farmers (Wen and Chen 2020 ). Second, digital transformation transforms agricultural production. The addition of digital production factors change the practices of traditional agricultural production, which are predominantly guided by human experience and promotes the transformation of crude and non-standard traditional agricultural practices to standardised and accurate agricultural production. These benefits advance the improvement of agricultural production efficiency and the quality and safety of exported agricultural products (Sun et al. 2023 ). Second, digitalisation enables technical information sharing. Digital technologies such as the internet promote users’ access to explicit and implicit knowledge and technology sharing (Grant et al. 2010 ), and enterprises can employ digital technologies to access and learn to navigate new agricultural technology resources such as Good Agricultural Practices audit implementation, enhanced planting and cultivation practices and external knowledge on the rational use of pesticides and chemical fertilisers and introduction to new technology and promote the continuous optimisation and innovation of agricultural products, promoting continuous quality improvement of exported agricultural products. Therefore, this study proposes the following:

Hypothesis 2: Digital transformation improves the quality and safety of exported agricultural products through technological innovation effect.

Product traceability effect

First, digitalisation enables the traceability of agricultural products’ entire production chain. By using digital technology, enterprises can establish a complete archive and information database of the production process, recording key data and information regarding all aspects of agricultural products’ planting, breeding, production, processing, packaging and transport. This information can help enterprises trace the source, production conditions, direction of flow and other important information of agricultural products to strengthen quality control and ensure quality and safety (Tan et al. 2015 ). Agricultural export enterprises face risks such as freshness and obstruction of traffic and logistics in transport, and digital transformation can improve agricultural export enterprises’ information circulation efficiency, enable enterprises to obtain timely and effective market information and logistics information more expediently, facilitate efficient communication between upstream farmers and downstream enterprises, conduct risk prediction and prevention and reduce supply chain risk (Song et al. 2023 ). These benefits subsequently ensure enterprises’ production efficiency and improve the quality and safety of export products. Second, digitalisation enables rapid recall and location of agricultural products’ problems. When quality and safety problems arise for exported agricultural products, enterprises can rapidly locate and investigate the root causes of food safety problems using efficient digital traceability system, expediently implement corrective measures, implement precise recall or treatment measures, reduce the quantity and scope of affected agricultural products and protect the rights and interests of consumers (Rauniyar et al. 2023 ), which leads to the hypothesis:

Hypothesis 3: Digital transformation improves the quality and safety of exported agricultural products through product traceability effect.

Information sharing effect

Through digital transformation, companies can share more information with consumers regarding product origins, quality and production processes, increasing product transparency and traceability, reducing food safety issues and promoting the production and export of high-quality agricultural products. First, digital traceability information sharing can be implemented as enterprises can establish a supply chain system with higher transparency and traceability using digital technology (Zhang and Gu 2023 ). By scanning the QR code on the product packaging or using mobile phone apps and other digitally enabled techniques, consumers can obtain information on the entire trajectory of agricultural products’ planting, breeding, production and processing in addition to quality test results, including data on pesticide residues, heavy metal content, nutrient content and other relevant considerations. This transparency effectively alleviates the information asymmetry between producers and consumers and increases consumers’ trust in product quality and safety (Cuesta et al. 2013 ). Companies can also employ big data analytic to monitor and analyse key indicators in the production process in addition to market and consumer behaviour data. Using this information, enterprises can proactively establish early warning systems to early detection systems for potential food safety issues and implement appropriate measures to intervene and improve the quality of exported agricultural products. Second, digital transformation enables companies to expediently collect, analyse and respond to consumer feedback and monitoring information (Zhang et al. 2023 ). Consumers can share opinions and suggestions on the quality of exported agricultural products and food safety through social media, online surveys, evaluation platforms and other channels. Enterprises can use this information to quickly identify and address potential problems and apply measures to improve the quality of exported agricultural products and production processes. Therefore, we propose the following hypothesis:

Hypothesis 4: Digital transformation improves the quality and safety of exported agricultural products through information sharing effect.

Quality assurance effect

Enterprises can use digital technology to establish a digital product certification system to manage and verify agricultural products’ quality certification and labelling information more efficiently and accurately, enhance market trust and reduce the occurrence of food safety incidents (Wang et al. 2023 ). First, quality assurance systems ensure the authenticity and integrity of agricultural products’ certification information. Compared with paper certifications, electronic certification uses technical means such as encryption algorithms and digital signatures, reducing the risk of tampering and facilitating traceability and verification (Dogui and Ivanov 2022 ) and guaranteeing the quality and safety of exported agricultural products. Second, enterprises can present quality certification and labelling information for consumers on digital platforms, demonstrate product quality through visual elements that confirm safety information in the form of certificates and certification marks and externalise intrinsic quality information of the products, transforming the attributes of agricultural products from trusted goods to searched goods (Hong and Cho 2011 ). Consumers in importing countries can verify the certification status of the products online, providing real-time information regarding the products’ characteristics and quality, which improves the level of food safety compliance and enhances the credibility and competitiveness of the exported agricultural products. Therefore, this study proposes the final hypothesis:

Hypothesis 5: Digital transformation improves the quality and safety of exported agricultural products through quality assurance effect.

Model setting, variable construction and data sources

To quantify the impact of agricultural export enterprises’ digital transformation on the quality of exported agricultural products, based on the theoretical analysis above, this study adopts the two-way fixed effect model and uses the staggered DID model for empirical testing (Nunn and Qian 2011 ). The basic idea for this approach is that because enterprises have undergone digital transformation at different points in time and to varying degrees, ordinary DID models cannot measure the change and degree of digital transformation. This study subsequently adopts a staggered DID model for testing, setting enterprises with no digital transformation as the control group and enterprises with any degree of digital transformation as the experimental group. In the sample observation period, most enterprises’ degree of digital transformation has undergone a shift from 0 to non-0, in alignment with the design of the intensity variable in the staggered DID, which provides a better quasi-natural experimental environment for this study, and the specific model is constructed as follows:

Where \(f\) , \(k\) , \(j\) and \(t\) denote the enterprise, product, destination country and year respectively. The explanatory variable \(qua\_sa{f}_{fkjt}\) is the quality and safety of agricultural products exported by the enterprise, the core explanatory variable \(digita{l}_{ft}\) is the degree of digital transformation of the enterprise \(f\) in the year \(t\) , \(pos{t}_{ft}\) indicates whether the enterprise has undergone digital transformation in the year \(t\) , if yes, then it takes 1, otherwise it takes 0; \(Contro{l}_{kjt}\) represents the control variables. This paper also controls the two-dimensional combination of the enterprise-product fixed effect \({\delta }_{ik}\) , the two-dimensional combination of the destination country-product fixed effect \({\delta }_{jk}\) , and the firm-destination country fixed effects \({\delta }_{jk}\) , and further controls for product-year fixed effects \({\delta }_{kt}\) , so as to control for all individual effects related to firms, products and destinations that do not change over time; \({\varepsilon }_{fkjt}\) is the error term, and \(\beta\) is the core estimation parameter representing the net effect of the impact of firms’ digital transformation on the quality and safety of China’s exported agricultural products.

Measurement and description of variables

Explained variable.

The explanatory variable is the quality and safety level of exported agricultural products ( \(qua\,{\_}\,sa{f}_{fkjt}\) ). Since the concept of ‘Food Safety’ was put forward by the Food and Agriculture Organisation of the United Nations in 1974, food safety can be divided into two levels: Food security and Food safety. Food security refers to the availability of adequate food at the global, national and regional levels, and household levels (Pinstrup-Andersen 2009 ). Food Safety, mainly from the perspective of food hygiene and safety, requires that food should avoid the threat of food-borne diseases in the production process (Kirch 2008 ). In demonstrating the practicability and feasibility of systematic evaluation of food and feed safety, experts from the European Food Safety Authority (EFSA) believe that food safety is a multi-faceted concept that needs to be comprehensively considered from the four perspectives of human health, plant health, animal welfare and environment (Aiassaa et al. 2015 ). Food safety is a macro concept involving many factors. At present, food safety is defined through the three dimensions of quantity safety, quality safety and sustainability, and an indicator system for evaluating food safety is built on this basis, which has gained wide consensus. For example, the food safety evaluation index system built by the Economist takes quantitative safety, quality safety and sustainability as three first-level indicators.

The quality and safety of agricultural products covers all the quality attributes of agricultural products and highlights the safety attributes, highlighting the overall quality safety concept of agricultural products management. The quality and safety information of agricultural products is the effective information that can reflect the quality characteristics of agricultural products such as the safety of agricultural products, packaging of agricultural products and production process of agricultural products. Therefore, this paper uses content analysis to define and measure the quality and safety information disclosure of sample enterprises, in accordance with the provisions of the national food safety standard ‘General Hygiene Standards for Food Production’ (GB 14881-2013), referring to the social responsibility index system in Guide 3.0 for Food Enterprises issued by the Chinese Academy of Social Sciences, and with reference to the research of Alix-Garcia et al. ( 2013 ), Sumner and Ross ( 2002 ), Chen ( 2016 ) and Cheng et al. ( 2019 ). Based on the perspectives of food quality and safety assurance, food quality and safety information disclosure and customer responsibility, the words ‘traceability’, ‘product quality’, ‘certification’, ‘risk management’ and ‘food safety’ were retrieved from the annual report, internal control self-evaluation report and social responsibility report of the sample enterprises, from the four dimensions of quality traceability, information communication, quality control and risk prevention. A total of 18 indicators were used to measure the quality and safety level of the sample enterprises (see Table 1 ). Score item by item according to the actual disclosure situation of the quality safety level of export agricultural products of the enterprise, and assign 1 value to each disclosure content, that is, if the sample enterprise discloses one of the indicators, assign 1 value, otherwise, 0 value, and summarise the score value of the quality safety level of export agricultural products of the enterprise. As enterprises attach different importance to core and peripheral products, enterprises will tilt internal resources and management focus to core products, adjust product mix and improve the quality of core products (Sun et al. 2022 ), including more and better production factors and a larger share of R&D investment, so as to improve the quality of core products. Therefore, this paper takes the export value as the weight, and refines the quality and safety level of export agricultural products from the enterprise level to the product level.

Policy variable

The Policy variable is enterprise digitalisation transformation index ( \(digita{l}_{ft}\) ). This study uses the Python crawler function to identify keyword word frequencies in the annual reports of listed companies exporting agricultural products, constructs a thesaurus and quantifies the word frequencies to determine listed companies’ degree of digital transformation (see Table 2 ). This measurement method makes up for the insufficiency of dummy variables used in previous studies, quantifies the differences in digital transformation intensity and also establishes a suitable data environment for conducting a quasi-natural experiment with staggered DID (Yuan et al. 2021 ). Based on the above analyses, we use the text analysis method to delineate indicators of enterprises’ degree of digital transformation. Specifically, in the initial step, we first screen keywords indicating digital transformation from policy documents on advancing the digital economy released by the state, digitalisation themes and literature related to agricultural digitalisation. According to the research intent of this study, words related to food safety, agricultural production and agricultural trade are then selected from the keywords of digital transformation. In the second step, we supplemented the keyword thesaurus by examining agriculture- and digitalisation-related words that appear more frequently in the annual reports of listed companies. In the third step, we referenced the 2021 Research Report on the Digital Transformation of Central Enterprises, the 2022 Research Report on the Digital Transformation of Chinese Private Enterprises and the 2022 Report on the Development of China’s Digital Countryside, dividing the keywords into five dimensions according to ‘digital technology application’, ‘digital information system’, ‘digital intelligent management’, ‘digital marketing model’ and ‘digital efficiency improvement’. In the fourth step, using the keyword thesaurus formed in the above steps, we count the frequency of words involving the above keywords in the annual reports of listed companies exporting agricultural products and take the logarithm of the frequency of the words to establish an indicator of enterprises’ degree of digital transformation ( \(digita{l}_{ft}\) ), where a larger the indicator value indicates a higher degree of enterprise digital transformation.

Control variables

This paper also controls for other variables that affect the quality and safety of exported agricultural products, of which \(SP{S}_{kjt}\) is the importing country’s SPS measure, measured by the number of notifications made by the importing country to the HS 2-digit code level in the period of \(t-1\) ; \(ope{n}_{jt}\) is the importing country’s level of openness to the outside world, which is expressed by the importing country’s total imports and exports in terms of the share of its GDP, and is used to measure the relevance of the importing country to the outside economy; \(pgd{p}_{jt}\) is the importing country’s level of per capita income, which measures \(exchang{e}_{jt}\) is the exchange rate of RMB, which is converted using the US dollar as an intermediary measure to control the impact of trade costs; tariffs of importing countries’ products ( \(tarif{f}_{jt}\) ) are expressed as tariff rates corresponding to HS6-coded products, which are used to control the impact of tariff barriers, and missing values are replaced by tariff rates of HS4- or HS2-coded industries; geographic distance ( \(distanc{e}_{jt}\) ) is measured as the geographic distance between the capitals of China and the importing countries.

Data description and descriptive statistics

The data used in the empirical research of this study are obtained from the China Customs Database, the Cathay Pacific Financial and Economic Database (CSMAR), the RESSET Financial Research Database and listed companies’ annual financial reports. Considering that all listed companies began to implement the new accounting standard system on 1 January 2007, and some indicators are only counted from 2007 onwards and currently available Chinese Customs data cover 2000–2016, to ensure the consistency of our data indicator measurements, this study uses data from 2007 to 2016 for the study. After matching the above data, we cleaned the data as follows. (1) Excluding financial, ST and *ST enterprises, and retaining only A-share listed companies. (2) Excluding data with missing values for key indicators such as total assets, revenue and number of employees or data that do not comply with accounting rules. Among the control variables, the data for importing countries’ per capita GDP, population size, degree of openness to the outside world, product tariffs and exchange rates are obtained from the World Bank database, geographic distance data are obtained from CEPII-GeoDist database and SPS measures data are obtained from the World Trade Organisation’s notification system for SPS measures. Descriptive statistics of the variables are detailed in Table 3 .

Empirical results and analysis

Typical facts and a priori judgements.

In order to reflect more intuitively the changes in the quality of exported agricultural products in the experimental and control groups over the sample period, this paper uses curves to portray the trends in the average quality index of exported agricultural products in the experimental and control groups, respectively, as shown in Fig. 2 . China’s substantial policies regarding the development of digital transformation began in 2013, and the creation of new types of digital economy businesses mainly occurred after this (Ma et al. 2015 ; Du and Zhang 2021 ). As can be seen from Fig. 2 , before 2013, there were fluctuations in the quality index of exported agricultural products in the experimental group and the control group, and after 2013 the quality index of exported agricultural products in the experimental group and the control group generally showed an upward trend, and for the experimental group, the trend of growth in the quality of the export was significantly stronger than that of the control group. Among them, the experimental group’s export agricultural product quality index increased more after 2013, indicating that the quality of export agricultural products was affected by factors such as digital transformation and changes in the international trade environment. As an ex ante test, Fig. 2 reflects from the side that the difference in the change in the quality of exported agricultural products between the experimental group and the control group is correlated with the digital transformation of enterprises, which provides an a priori judgement for this paper’s empirical research using the staggered double-difference model.

Trends in the quality and safety level of exported agricultural products.

Parallel trend test

The DID model requires that the data satisfy the parallel trend assumption that prior to firms’ digital transformation, digitally transformed (treat group) and non-digitally transformed (control group) firms essentially maintained the same trend in terms of changes in export quality. Under this assumption, changes that occur in exported agricultural products’ quality after firms’ digital transformation can then be considered as the effect of policy intervention. This study references Beck et al. ( 2010 ), examining the dynamic changes in the quality of exported agricultural products before and after enterprises’ digital transformation. If the quality of exported agricultural products did not improve significantly before the digital transformation of enterprises, but improved significantly after the transformation, this indicates that this improvement is indeed attributable to digital transformation, and the conclusions drawn from the baseline regression are plausible. Considering the limitation of data length, this study selects four years prior to mutual recognition and three years following mutual recognition to conduct the dynamic trend test, establishing the fixed effect model shown below:

In Eq. ( 10 ), \(n=t-year\) and \(year\) denote the year of the enterprise’s digital transformation shock, and \({D}_{fn}\) is a dummy variable; if the enterprise \(f\) is a digitally transformed enterprise and the year is \(year\) from the year of the transformation shock, \({D}_{fn}\) is set to take the value of ‘1’, otherwise it is ‘0’. Here, the time interval before and after the transformation impact is narrowed to the first 4 and the last 3 periods Footnote 7 , so that \({D}_{in}\) is a set of variables including \([{D}_{i(-4)},{D}_{i(-3)},\mathrm{..}.,{D}_{i(0)},\mathrm{..}.{D}_{i(3)}]\) . The remaining variables in Eq. ( 10 ) have the same symbolic meaning as in Eq. ( 9 ). The parallel trend test focuses on the changes in a series of coefficients \({\xi }_{n}\) .

Based on the size and significance of the economic effect in each period in Fig. 3 , the positive impact effect in each period after digital transformation is greater, changing from an insignificant effect to a significant effect, confirming that before digital transformation, no significant difference is evident between the transformed and non-transformed enterprises in the quality of exported agricultural products. In contrast, after the transformation shock, the quality of the exported agricultural products of the transformed enterprises compared to the non-transformed enterprises significantly improved, indicating the effectiveness of digital transformation. In terms of the trend of change in the effect of digital transformation, the positive impact effect increasingly rises, which lasts until the third period after the digital transformation, indicating that enterprises’ digital transformation has a medium- to long-term effect in promoting the quality of exported agricultural products.

Dynamic effects of quality and safety of exported agricultural products.

Estimated results of the benchmark regression

Considering that the occurrence of ‘zero trade flow’ prevails in reality due to excessive trade costs, and that the trade impact identification model of enterprise digital transformation includes fixed effects at country, enterprise, product and time levels, we reference Correia et al. ( 2020 ), testing the impact of enterprises’ digital transformation on the quality of exported agricultural products using Poisson pseudo-maximum likelihood method and Stata software. The regression results are presented in Table 4 .

Examining the baseline regression analyses in Table 4 , the coefficients of digital in columns (1)–(3) are significant and positive after the inclusion of control variables and fixed effects variables, indicating that enterprises’ digital transformation significantly improves exported agricultural products’ overall quality and alleviates the food safety concerns of exported agricultural products, which improves the quality of China’s exported agricultural products, supporting Hypothesis 1. For example, Yantai Shuangta Foods Co., Ltd, which is a leading manufacturer of Longkou vermicelli, focuses on the digital economy and uses enterprise big data, establishing an information technology software system, information technology hardware and the fusion of digitalisation and business operations to develop an internal data source for the enterprise. Through digital empowerment, the company realises the fusion of digital and production management, successfully making the ‘green factory’ list, which is a national green food manufacturing benchmark for enterprises. From 2023 January to October, Yantai Shuangta Foods exported 820 million yuan in product export value, representing an average annual growth rate of 5% Footnote 8 .

The coefficients of the control variables are in line with expectations, with positive coefficients for the variables of GDP per capita in the importing country and the degree of openness to the outside world, indicating that a high level of economic level in the importing country and a high degree of openness to the outside world can help to improve the quality and safety of China’s agricultural exports. The negative coefficients on the variables of tariffs on products from importing countries, RMB exchange rate, and geographical distance indicate that high tariffs in importing countries, RMB appreciation, and China’s distance from importing countries hinder the quality upgrading of China’s exported agricultural products.The coefficient of the SPS on the upgrading of agricultural products is uncertain, possibly because the effect of SPS measures on quality upgrading depends on the magnitude of the cost of compliance and the cost of market shifting (Liu and Dong 2021 ).

Robustness tests

This study conducts five robustness tests to ensure the accuracy of the baseline regression results.

Dependent variable replacement

In this paper, the quality of the current period is worse than that of the previous period to represent the quality upgrade ( \(qualit{y}_{fjkt}^{\text{'}}\) ). As for the measurement of product quality, according to the research of Khandelwal et al. ( 2013 ) and Shi ( 2014 ), Eq. ( 11 ) is regression:The results presented in column (1) of Table 5 are basically the same as those of the benchmark regression, validating that the benchmark regression results are robust.

Where \({q}_{fjkt}\) and \({p}_{fjkt}\) are the number of products exported by the firm and the price of the exported products, \({\sigma }_{k}\) is the elasticity of substitution of the product \(k\) , \({\delta }_{k}\) and \({\delta }_{jt}\) are the product fixed effects, time fixed effects of the importing country, and \({\varepsilon }_{fjkt}\) is the residual component. Using the sample data of price and quantity, OLS regression of the above equations gives the quality of the product being estimated, which is expressed in the form:

The final expression for product quality can be obtained by normalising the results of Eq. ( 12 ),

Where \(maxqualit{y}_{kt}\) and \(minqualit{y}_{kt}\) denote the maximum and minimum values of the quality of the product \(k\) exported to all destination countries in the year \(t\) , respectively, and \(qualit{y}_{fjkt}^{\text{'}}\) is the quality of the firm \(f\) exporting the product \(k\) to the country \(j\) in the year \(t\) .

Sample shrinkage and truncation treatment

To effectively avoid the impact of outliers on the estimation results, this study references Crinò and Ogliari ( 2015 ), conducting bilateral shrinking and bilateral truncation for the sample (i.e. all the results in the 1% and 5% quartiles are directly excluded as outliers, and the re-estimating Eq. ( 9 )). Combined with the results in columns (2) and (3) of Table 5 , digital is basically consistent with the regression results in Table 3 in terms of coefficient size, sign and significance, further verifying our benchmark results.

Overcoming sample selection bias

We next apply the Heckman two-step approach to overcome sample selection bias. We use whether firms had exporting behaviour in the previous period as the exclusion variable (Chatterjee et al. 2013 ) and the test results are reported in column (4) of Table 6 , with significant coefficients on the inverse Mills ratio, indicating that firms’ digital transformation still significantly improves the quality upgrade of exports.

Digital transformation shock time selection

In order to test the effectiveness of digital transformation time point selection, on the one hand, the digital transformation time point is set as two years before the digital transformation time, one year before and one year after the digital transformation time for testing. The results of columns (1)–(3) in Table 6 show that the two years before the digital transformation time and one year before the digital transformation time have no significant impact on the quality and safety level of export agricultural products. One year after the digital transformation, the quality and safety level of export agricultural products had a positive promoting effect.

Placebo test

To test whether the effects of digital transformation derived above are potentially driven by unobservant factors at the country-product-year level, we next conduct a placebo test by randomly assigning mutually recognised products (Cai et al. 2016 ). Firms are randomly selected as the treatment group and assumed to have undergone digital transformation, while others are non-digitally transformed firms, establishing ‘pseudo’ treatment and control groups. In this paper, the quality upgrading of China’s exported agricultural products is regressed 1000 times as an explanatory variable. The estimated coefficients of digital in column (4) of Table 6 are insignificant, once again confirming that the baseline regression results are robust.

Endogeneity test

Considering that firms exporting high-quality products can be considered to have particular incentives to take the initiative to implement digital transformation, this creates a potential two-way causation problem. To address the potential reverse causation problem, this study uses a lagged period of digital transformation data, which is based on the fact that since enterprises’ digital transformation is a continuous process, the degree of digital transformation in the previous year is the basis for the digital transformation of the current year, and at the same time, a certain time lag effect of the impact of digital transformation on exports is expected. Therefore, the degree of digital transformation in the previous year should have an impact on exports in the current year, but the enterprise’s exports in the current year will not impact the digital transformation of the previous year. The results of the test are as shown in column (1) of Table 7 .

To address the possible omitted variables problem, this paper uses the instrumental variable two-stage least squares (IV-2SLS) method for testing. First, referencing Du et al. ( 2022 ), we use the density of long-distance fibre-optic cable lines in the province where the listed company is located as an IV for enterprise digital transformation (i.e. instrumental variable = long-distance fibre-optic cable lines in the province where the listed company is located/area of the host province and city). First, Internet access and continuously updated data are the crucial components of enterprises’ digital transformation, and long-distance cable lines are important infrastructure for data transmission, where denser long-distance cable lines in a province indicate better the digital infrastructure in the province and a higher degree of satisfaction of the external conditions for enterprise digital transformation. Therefore, the degree of enterprise digital transformation is highly correlated with the density of long-distance cable lines in the province where the company is located. Second, the density of long-distance cable lines is a function of the area of the province where the listed company is located, and the density of long-distance cable lines is controlled by the four major network operators in China, meaning that enterprises cannot change or control the density of long-distance cable lines according to their own needs. Thus, the density of long-distance cable lines cannot impact the export scale or product quality of the enterprises in this province, which meets the conditions for the use of IVs. The results of the test are presented in column (2) of Table 7 .

Second, as listed companies are distributed in various cities, and even in the same province, and each city has differences in development, we reference Huang et al. ( 2019 ) and use the number of post offices per million population in each city in 1984 as an IV for firms’ digital transformation, and further introduce city fixed effects. Historically, post and telecommunications have been important means of communication, and the number of post and telecommunications in history is expected to affect the local acceptance of information technology, with an impact on the application and promotion of information technology in the local area. Therefore, a certain degree of correlation is assumed between the number of post and telecommunications in a city and firms’ digital transformation. Furthermore, post and telecommunications are social and public service facilities focusing on the provision of communications for the general public; therefore it does not have an impact on enterprises’ export scale and product quality, satisfying the conditions for the use of instrumental variables. Since the 1984 post and telecommunications data are cross-sectional data, while the data used in this study are panel data, we reference Zhao et al. ( 2020 ), using and the cross-multiplier terms of the number of post and telecommunications per million people in each city in 1984 and the number of people who have accessed the internet nationwide in the previous year as the IV data for enterprises’ digital transformation. The results of the test are shown in column (3) of Table 7 , revealing that the impact of firms’ digital transformation on the quality of exported agricultural products remains significantly positive, indicating that the estimation results are still robust after addressing endogeneity problems caused by reverse causation and omitted variables. In addition, in the estimation results of IV method, the p -values of Kleibergen-Paap rk LM statistics are all 0, rejecting the original hypothesis that IVs are not identifiable at the 1% level. In addition, the Kleibergen-Paap rk Wald F-values are all greater than the 10% critical value of 16.38; thus, the original hypothesis of weak IVs is rejected, indicating valid IVs.

Heterogeneity test

Heterogeneity in the level of export destination countries’ economic development.

The impact of enterprises’ digital transformation on upgrading the quality of exported agricultural products may also vary depending on export destination countries’ economic development. We examine the impact of firms’ digital transformation on the quality of agricultural exports from developed and developing countries separately according to the World Bank’s classification of developed and developing countries. Columns (1) and (2) of Table 8 reveal that the impact of firms’ digital transformation on the quality of agricultural products exported to developed and developing countries is positive and has a greater impact on exports to developed countries than developing countries. The possible rationale for this outcome is that in countries with a high level of economic development, consumers’ shopping habits and behavioural patterns tend to be more online, which provides a new sales channel for digitally transformed firms exporting agricultural products, through which they can directly reach consumers, reduce sales costs and improve transparency and transaction efficiency. This means more sales channels and higher sales efficiency for digitally transformed enterprises, which improves the quality of exported agricultural products. In addition, consumers in developed countries usually have more disposable income to spend on high-quality agricultural products and are more concerned about the quality, safety and nutritional value of the food, agricultural production methods and the impact on the environment and animal welfare.

Heterogeneity of exporting firms

At the level of export enterprises, the regions where export enterprises are located and enterprise ownership are important factors that can affect enterprises’ digital transformation of upgrade the quality of exported agricultural products. Among them, for the region where the export enterprises are located ( \(area\) ), considering the differences in the digital development of enterprises in different regions, this study divides the sample into eastern, central and western regions according to the region where the enterprises are located to conduct regressions. For enterprise ownership ( \(ownership\) ), this study divides the enterprises into state-owned and non-state-owned samples for regression according to the nature of the actual controller of the enterprise. The results in columns (3)–(5) of Table 8 show that the coefficient of enterprise digital transformation on the quality of exported agricultural products in the eastern region is significantly positive, while the regression results for enterprises in central and western regions are not significant. The possible reasons for this are that with the higher level of economic development in the eastern region, which generally has a leading role in the development of high-end digital industries, with more complete information infrastructure and more advanced technology, enterprises can obtain more opportunities for digital development, and also have access to better external agricultural resources and technical support, while the economic development of the central and western regions is relatively slow, with a relative lack of digital talent, technology and agricultural resources; thus, enterprises’ ability to obtain more digital development opportunities are relatively scarce, resulting in differences in enterprise development. The results in columns (6) and (7) of Table 8 show that the digital transformation of both state-owned and non-state-owned enterprises has a significant upgrading effect on the quality of exported agricultural products; however, the quality upgrading effect is greater for non-state-owned enterprises. The management, operation mechanism and corporate culture of state-owned enterprises are more inclined towards maintaining operational stability and security, and decisions to conduct digital agricultural production and activities will be relatively cautious and conservative; thus, the degree of digital transformation is lower. In addition, because the main body of agricultural exports are from state-owned enterprises, which have relative advantages in policy support, government subsidies, credit financing and other support, the agricultural exports of state-owned enterprises are subject to less competitive pressure (Shen et al. 2012 ). This will lead to the lack of intrinsic incentives for state-owned enterprises to innovate in agriculture, affecting improvement in the quality of exported agricultural products.

Heterogeneity of agricultural product types

We next examine the differences in the impact of digital transformation of enterprises on the quality of exported agricultural products are examined based on types of exported agricultural products. First, we classify exported products into bulk, intermediate, consumer-oriented and other related agricultural products Footnote 9 . Second, in terms of export product quality, we calculate the average product quality of each firm during the sample period, classifying the top one-third of products with the highest product quality in each HS 2-digit code as high-quality products, and the rest as medium- and low-quality products in a sub-sample regression.

The results in columns (1) and (2) of Table 9 show that firms’ digital transformation enhances the quality of low- and medium-quality agricultural products more than that of high-quality agricultural products. There is more room for improvement of lower quality products, and firms’ digital transformation will promote them more; thus, digital transformation is more likely to affect low- and medium-quality agricultural products. The regression results in columns (3)–(6) of Table 9 show that the effect of digital transformation on the quality of exported bulk and consumer-oriented agricultural products is significantly positive, while that on intermediate and other related agricultural products is not significant. The possible reason for this is that bulk and consumer-oriented agricultural products have high standards and requirements in all aspects of the production process, processing and packaging and the application of digital technology can improve the quality and safety performance of these products, obtaining a higher market value. For intermediate and other related agricultural products, the impact of digital transformation is relatively small because the quality and safety performance of these products are relatively low, and the application of digital technology has limited effect on improvement. In addition, the market competitiveness of these products primarily depends on market demand and price factors, and the application of digital technology has limited impact on market demand and price.

Mechanism of action test

Model setting.

Our findings demonstrate that digital transformation of firms facilitates the quality upgrade of exported agricultural products. The question that arises is through what mechanism does this process occur? This paper draws on the research of Jiang ( 2022 ) to further investigate whether enterprise digital transformation will contribute to the quality upgrading of export agricultural products through the product traceability effect, technological innovation effect, information sharing effect and quality assurance effect, and the model is constructed as follows:

In Eq. ( 14 ), \({T}_{fkjt}\) represents the proxy variables for the technological innovation effect ( \(tech\,{\_}\,in{n}_{fkjt}\) ), product traceability effect ( \(pro\,{\_}\,trac{e}_{fkjt}\) ), information sharing effect ( \(inf\,{\_}\,shar{e}_{fkjt}\) ) and quality assurance effect ( \(qua\,{\_}\,as{s}_{fkjt}\) ), respectively, and the rest of the variables are consistent with the benchmark regression, with the coefficient \({\beta }^{\text{'}}\) being the core coefficient of interest in this paper.

Description of variables

Technological Innovation Effect ( \(tech\,{\_}\,in{n}_{fkjt}\) ). In this paper, we use the research and development (R&D) investment intensity (RD) of enterprises, i.e., the logarithm of the R&D investment of enterprises in the current year, to measure as a proxy variable for enterprise technological innovation, and take the natural logarithm after adding 1 to it. At the same time, the improvement of innovation level as well as technology introduction will lead to technological progress, so this paper refers to Sheng and Mao ( 2017 ), and also uses the technological complexity of export products as a proxy variable for enterprise innovation to further explore the mediating effect.

Product traceability effect ( \(pro\,{\_}\,trac{e}_{fkjt}\) ). In this paper, the statistics of the electronic certification mark displayed on the official website of the enterprise and Wechat public number are carried out, including the electronic certification certificate of agricultural products, the green certification of agricultural products, the organic certification, the geographical indication certification and other picture information, and the number of pictures, videos, and two-dimensional code information that provide the electronic certificate certification are cumulatively summed up, and the product traceability index of the enterprise is obtained in the end.

Information sharing effect ( \(inf\,{\_}\,shar{e}_{fkjt}\) ). The opening of the official website of the enterprise can facilitate consumers to understand the production process of the enterprise, and understand the relevant raw material procurement, agricultural production and processing information of the enterprise in a more graphic manner, and the establishment of the enterprise’s applet is a reflection of the enterprise’s willingness to communicate with consumers and the degree of information sharing. Therefore, this paper measures the information sharing effect through the opening of enterprise homepage and applets.

Quality assurance effect ( \(qua\,{\_}\,as{s}_{fkjt}\) ). An enterprise’s product quality assurance capability can be measured by establishing a sound quality management system, setting up product files, actively participating in certification assessment, and utilising technological means (Guo and Xiao, 2022 ). Therefore, this paper applies whether the enterprise obtains quality management certifications such as ISO9001, ISO22000, HACCP, and product certification information such as QS and CCC to measure the product traceability effect, and if it is, then it takes 1 and sums up to obtain a proxy variable for the quality assurance effect.

Mediating mechanism test

The test results for technological innovation and product traceability effects of enterprise digital transformation are presented in Table 10 . In terms of the technological innovation effect, the impact of enterprise digital transformation on R&D investment intensity is significantly positive, and enterprise digital transformation promotes innovation, which subsequently promotes upgrading the quality of exported agricultural products, supporting Hypothesis 2. Digital transformation facilitates enterprises’ acquisition of new agricultural technologies and enhances coordination and resource sharing in all aspects of agricultural production, which strengthens enterprises’ innovation and ultimately improves the quality of exported agricultural products. For example, Fuling Squash constantly pushes forward and focuses on the entire industry chain of squash, opening up the data flow of green beetroot planting and acquisition, salt vegetable block processing and sales and squash marketing, among other activities, promoting quality improvement through technological and product innovation. The export volume of Fuling Squash is expected to reach 100000 tonnes, with an output value of more than 1.5 billion yuan by 2027 Footnote 10 .

The effect of enterprise digital transformation on product traceability is significantly positive, supporting Hypothesis 3. The focus of agri-food enterprises is how to form a closed loop of the entire chain of quality management; for example, New Hope Dairy was the first in the industry to engage in digital transformation and upgrade, developing the digital quality management tool Fresh Source and the digital supply chain system Shipping Lychee, to trace the source of products, and launching the digital supply chain system Litchi, as the first in the industry. The company also launched the digital marketing tool Fresh Go, and the Lighthouse Factory, making food production more transparent, intelligent, efficient and flexible, to achieve industry chain visualisation, transparency and product traceability, effectively guaranteeing the quality and safety of its dairy products Footnote 11 .

The impact of enterprise digital transformation on information sharing is significantly positive, indicating that enterprise digital transformation promotes export quality upgrading by improving information sharing capacity, which supports Hypothesis 4. Digital transformation enhances information sharing in agricultural production and export links, which improves the quality and international competitiveness of agricultural products. For example, Shandong Dong’a Gum Co. comprehensively combined 5G convergence application areas and built a new retail platform for customers, an ‘internal marketisation’ platform for employees and a ‘creativity platform’ for social participation, launching the Freshly Made Ready-to-Eat customisation service, with real-time production after customers have placed orders by means of 5G. Through 5G transmission, big data and cloud computing, Shandong Dong’a Gum Co. fulfills real-time production arrangements after customers place orders and interacts with customers in the process, which has led to a significant increase in online sales Footnote 12 . Dong’a products have passed all kinds of national sampling and flying inspections with high pass rates, and exports to Japan, passing the most stringent quality inspection by the Ministry of Health, Labour and Welfare of Japan, with 842 testing items, including pesticide residues, veterinary drug residues and heavy metals and bacteria, all of which are ‘zero detectable’ Footnote 13 .

In terms of quality assurance effects, Hypothesis 5 is supported by the assumption that firms’ digital transformation improves product certification and the quality of exported agricultural products. Through digital transformation, achieving quality certification becomes more efficient, accurate and reliable, which improves the quality and competitiveness of exported agricultural products. To provide the market and consumers with genuine Korla Scented Pears, Xinjiang Korla Scented Pear Co., Ltd. certified one million cases of Korla Scented Pears sold on its e-commerce platform with Chinese Inspection and Quarantine Agency (CIQ) traceability certification, with affixed CIQ traceability labels. Scanning the two-dimensional CIQ traceability code label provides origin information for Korla balsam pears, along with planting base, soil testing, product, quality testing, certification, manufacturers and dealers’ information Footnote 14 .

Conclusions and policy implications

Enhancing the quality of exported agricultural products and increasing trade added value is the key to establishing a new competitive advantage in exported agricultural products, building a trade powerhouse and achieving high-quality agricultural development. In this paper, based on the theoretical analysis of the mechanism of the impact of enterprise digital transformation on the quality and safety level of export agricultural products, using the data of Chinese listed companies and China Customs data from 2007 to 2016, with the help of Python crawler technology to portray the intensity of digital transformation of listed companies exporting agricultural products, and using the interleaved double difference method to explore the impact effect and mechanism of enterprise digital transformation on the quality and safety level of export agricultural products quality upgrading influence effect and mechanism. The study shows that (1) Enterprise digital transformation effectively improves the quality and safety of exported agricultural products, and this result holds after endogeneity, placebo and multiple robustness tests; (2) Heterogeneity analyses reveal that the quality and safety effect of enterprise digital transformation is greater for exporting to developed countries’ markets, non-state-owned enterprises and enterprises in the eastern region, in addition to bulk agricultural products and consumer-oriented agricultural products; (3) Mechanism analyses shows that enterprise digital transformation raises the quality and safety of exported agricultural products through technological innovation, product tracing, information sharing and quality assurance effects.

To further enhance the role of digital transformation in promoting the quality upgrading of enterprises exporting agricultural products, this study proposes three relevant policy recommendations.

First, under the trend of a new round of scientific and technological revolution and industrial transformation, China should accelerate the deep integration of digital technology and foreign trade entity enterprises, further increase the support for digital transformation of foreign trade enterprises, actively guide and help enterprises to achieve digital transformation, and break through the dilemma of ‘do not want to transform’, ‘cannot transform’ and ‘will not transform’. On the one hand, it is necessary to strengthen the construction of digital infrastructure, accelerate the construction of information network infrastructure, strengthen the support capacity of public services, and lay a solid foundation for the digital transformation of enterprises. On the other hand, it is necessary to increase the financial and financial support for enterprises’ digital transformation, realise the optimisation and upgrading of traditional production technologies, organisational processes and management methods, and improve the quality and safety level of enterprises’ export agricultural products.

Second, in the process of promoting the digital transformation of enterprises and the formulation of relevant policies, we should adhere to local conditions, policies based on enterprises, and step by step. For some enterprises with difficulties in transformation, the transformation threshold should be lowered, and appropriate support measures should be taken to lay a solid foundation for digital transformation of enterprises and provide more powerful support. At the same time, it is necessary to continue to consolidate the achievements of digital transformation in the eastern region, increase support for digital transformation in the central and western regions, and narrow the digital divide between regions.

Third, enterprise digital transformation is an important approach for addressing the problem of exported agricultural products’ quality and safety. Therefore, the government should issue relevant regulations to clarify the obligations and approaches for relevant enterprises to implement digital traceability of exported agricultural product quality and safety, implementing the requirements and functional settings of a safety traceability system for exported agricultural products and enhancing the capacity of intelligent supervision of agricultural product quality and safety to compel Chinese agricultural industries to upgrade the quality and safety of exported agricultural products.

Data availability

The data that support the findings of this study are available from the Experimental Teaching Centre for Intelligent Business of East China University of Science and Technology, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from authors upon reasonable request and with permission of the Experimental Teaching Centre for Intelligent Business of East China University of Science and Technology.

See the Outline for Building a Quality Country issued by the CPC Central Committee and The State Council, http://www.gov.cn/zhengce/2023-02/06/content_5740407.htm?share_token=B4AF8828-EB72-4ED4-8773-839D5EC45F63&tt_from=weixin_moments&utm_medium=toutiao_ios&utm_campaign=client_share&wxshare_count=1 .

Data source: Food violations in the second quarter of 2023, http://qingdao.customs.gov.cn/beijing_customs/ztzl1/jgjmzl/gzld43/5173307/index.html .

See the Obstruction of China’s exports of consumer goods, agricultural food and medical devices to Europe and the United States in the first half of 2023, http://shanghai.customs.gov.cn/beijing_customs/ztzl1/jgjmzl/gzld43/5282758/index.html .

See the Ministry of Commerce issued the 14th Five-Year Plan for High-quality Development of Foreign Trade, http://www.mofcom.gov.cn/article/xwfb/xwrcxw/202111/20211103220185.shtml?ivk_sa=1023197a .

See the Implementation Opinions of the Ministry of Agriculture and Rural Affairs on Implementing the Key Work Deployment of the CPC Central Committee and The State Council for Comprehensively Promoting Rural Revitalisation in 2023, http://www.gov.cn/zhengce/zhengceku/2023-02/22/content_5742671.html .

See ‘Cyberspace Administration of China releases Digital China Development Report ( 2020 )’, http://www.cac.gov.cn/2021–06/28/c_1626464503226700.htm?from=timeline .

In the parallel trend test, in order to avoid multi-collinearity, this paper deleted \({D}_{i}(-1)\) , that is, the first phase before the impact of digital transformation.

See ‘Twin Towers Food: Carrying the centurial Dream of Longkou Fans’, https://nongye.yantai.gov.cn/art/2023/11/20/art_20522_2919458.html .

According to the USDA Global Agricultural Trade System Online BICO classification standard for agricultural products, agricultural products can be classified into bulk agricultural products, intermediate agricultural products, consumer oriented agricultural products and other related agricultural products (mainly covering aquatic products).

Chongqing Fuling: To promote the production and output value of pickled mustard industry to continue to develop overseas markets, https://www.farmer.com.cn/2023/02/21/99921810.html .

New Hope Dairy Co., LTD. – Platform control digital traceability full link pain relief, https://www.cqn.com.cn/pp/content/2023-10/23/content_8991432.html .

5G new infrastructure enables digital marketing transformation, broadens application scenarios, and promotes industrial development, http://www.cac.gov.cn/2021-01/15/c_1612283173042149.html .

Dong ’e ejiao products were successfully selected into the ‘Good product Shandong’ brand, https://www.cqn.com.cn/zgzlb/content/2022-03/15/content_8795246.html .

Traceability has my taste and you – 1 million boxes of ‘Korla fragrant pear’ obtained ‘Traceability’ ID card, https://xj.ccic.com/index.php/article/908/1155.html .

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Acknowledgements

Funding for this research was provided by National Natural Science Foundation of China (Grant No. 71673087) and Ministry of Education of Humanities and Social Science of China (Grant No. 23YJA790017).

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Liu, Y., Dong, Y. & Qian, W. The impact of digital transformation on the quality and safety level of agricultural exports: evidence from Chinese listed companies. Humanit Soc Sci Commun 11 , 817 (2024). https://doi.org/10.1057/s41599-024-03321-w

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Facts and fiction about the social security earnings test.

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When you receive Social Security retirement benefits while working, you could run into one of the more complicated and confusing rules, the Social Security earned income limit, or earnings test.

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Only wages and salaries from jobs you work and your net profits from self-employment count toward the earned income limit. Earned income includes bonuses, commissions, and vacation pay. Income that doesn’t count towards the earnings limit includes pensions, annuities, investment income, interest, jury duty pay, and veterans’ or other military or government retirement benefits.

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The loss of benefits is only temporary. After you reach your FRA, the benefits that were forfeited in earlier years because of the earnings limit will increase your future benefits. Exceeding the earned income limit is more of a deferral or withholding of benefits than a loss of benefits.

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Run different scenarios through the calculators to get an idea of the likely results of your different choices.

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