non experimental quantitative research meaning

Quantitative Research with Nonexperimental Designs

by Janet Salmons, PhD Manager, Sage Research Methods Community

What is the difference between experimental and non-experimental research designs?

There are two types of quantitative research designs: experimental and nonexperimental. This introduction draws from The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation . Leung and Shek (2018) explain:

Experimental research design utilizes the principle of manipulation of the independent variables and examines its cause-and-effect relationship on the dependent variables by controlling the effects of other variables. Usually, the experimenter assigns two or more groups with similar characteristics. Different interventions will be given to the groups. In case there are differences in the outcomes among the groups, the experimenter can conclude that the differences result from the interventions that the experimenter performed. [Learn more about experimental design in an earlier Sage Research Methods Community post, Quantitative Research with Experimental Designs . ] Nonexperimental research designs examine social phenomena without direct manipulation of the conditions that subjects experience. Subjects are not randomly assigned to different groups. As such, evidence that supports the cause-and-effect relationships is largely limited.

There are two main types of nonexperimental research designs: comparative design and correlational design.

In comparative research, the researcher examines the differences between two or more groups on the phenomenon that is being studied. For example, studying gender difference in learning mathematics is a comparative research.

The correlational design is a study of relationships between two or more constructs. A positive correlation means that high values of a variable are associated with high values of another variable. For instance, academic performance of students is positively related to their self-esteem. On the contrary, a negative correlation means that high values of a variable are associated with low values of the other variable. For example, teacher–student conflicts are negatively related to the students’ sense of belonging to the school.

See how researchers use non-experimental research design in this multidisciplinary collection of open-access articles.

Open Access Comparative Studies

Humprecht, E., Hellmueller, L., & Lischka, J. A. (2020). Hostile Emotions in News Comments: A Cross-National Analysis of Facebook Discussions . Social Media + Society . https://doi.org/10.1177/2056305120912481

Abstract. Recent work demonstrates that hostile emotions can contribute to a strong polarization of political discussion on social media. However, little is known regarding the extent to which media organizations and media systems trigger hostile emotions. We content-analyzed comments on Facebook pages from six news organizations ( N = 1,800) based in the United States and Germany. Our results indicate that German news organizations’ Facebook comments are more balanced, containing lower levels of hostile emotions. Such emotions are particularly prevalent in the polarized information environment of the United States—in both news posts and comments. Moreover, alternative right-wing media outlets in both countries provoke significantly higher levels of hostile emotions, thus limiting deliberative discussions. Our results demonstrate that the application of technology—such as the use of comment sections—has different implications depending on cultural and social contexts.

Huang, J., Kumar, S., & Hu, C. (2020). Does Culture Matter? A Comparative Study on the Motivations for Online Identity Reconstruction Between China and Malaysia . SAGE Open . https://doi.org/10.1177/2158244020929311

Abstract. On social network platforms, people may reconstruct an identity due to various reasons, such as vanity, disinhibition, bridging social capital, and privacy concerns. This study aims to identify cultural differences in the motivations for online identity reconstruction between China and Malaysia. Data were collected from China and Malaysia using an online survey. A total of 815 respondents (418 Chinese and 397 Malaysians) participated in this study. Differences were found not only between Chinese and Malaysian participants but also among participants from different ethnic groups (e.g., the Malaysian-Malays and the Malaysian-Chinese). This study adds knowledge to the research concerning online identity reconstruction by taking into account national culture. It also extends the cross-cultural research concerning social network platforms and sheds light on the specific differences between Chinese and Malaysian participants. The findings of this study can help service providers to deploy specific strategies to better serve social network platform users from different countries.

Kalogeropoulos, A., Negredo, S., Picone, I., & Nielsen, R. K. (2017). Who Shares and Comments on News?: A Cross-National Comparative Analysis of Online and Social Media Participation . Social Media + Society. https://doi.org/10.1177/2056305117735754

Abstract. In this article, we present a cross-national comparative analysis of which online news users in practice engage with the participatory potential for sharing and commenting on news afforded by interactive features in news websites and social media technologies across a strategic sample of six different countries. Based on data from the 2016 Reuters Institute Digital News Report, and controlling for a range of factors, we find that (1) people who use social media for news and a high number of different social media platforms are more likely to also engage more actively with news outside social media by commenting on news sites and sharing news via email, (2) political partisans on both sides are more likely to engage in sharing and commenting particularly on news stories in social media, and (3) people with high interest in hard news are more likely to comment on news on both news sites and social media and share stores via social media (and people with high interest in any kind of news [hard or soft] are more likely to share stories via email). Our analysis suggests that the online environment reinforces some long-standing inequalities in participation while countering other long-standing inequalities. The findings indicate a self-reinforcing positive spiral where the already motivated are more likely in practice to engage with the potential for participation offered by digital media, and a negative spiral where those who are less engaged participate less.

Phillips, M., & Smith, D. P. (2018). Comparative approaches to gentrification: Lessons from the rural . Dialogues in Human Geography , 8 (1), 3–25. https://doi.org/10.1177/2043820617752009

Abstract. The epistemologies and politics of comparative research are prominently debated within urban studies, with ‘comparative urbanism’ emerging as a contemporary lexicon of urban studies. The study of urban gentrification has, after some delay, come to engage with these debates, which can be seen to pose a major challenge to the very concept of gentrification. To date, similar debates or developments have not unfolded within the study of rural gentrification. This article seeks to address some of the challenges posed to gentrification studies through an examination of strategies of comparison and how they might be employed within a comparative study of rural gentrification. Drawing on Tilly ( Big structures Large Processes Huge Comparisons . New York: Russell Sage), examples of four ‘strategies of comparison’ are identified within studies of urban and rural gentrification, before the paper explores how ‘geographies of the concept’ and ‘geographies of the phenomenon’ of rural gentrification in the United Kingdom, United States and France may be investigated using Latour’s ( Pandora’s Hope . London: Harvard University Press) notion of ‘circulatory sociologies of translation’. The aim of our comparative discussion is to open up dialogues on the challenges of comparative studies that employ conceptions of gentrification and also to promote reflections of the metrocentricity of recent discussions of comparative research.

Yaşar, H., & Sağsan, M. (2020). The Mediating Effect of Organizational Stress on Organizational Culture and Time Management: A Comparative Study With Two Universities. SAGE Open . https://doi.org/10.1177/2158244020919507

Abstract. This research was designed to investigate whether organizational stress had an intermediary role in the effect of Hofstede cultural dimensions on time management. Near East University from Cyprus, which represents the individual culture, and Hakkari University from Turkey representing the collectivist culture were selected for the research analyses. In all, 638 administrative and academic members from both universities were interviewed face-to-face on a voluntary basis, and data were collected by the simple random sampling method. The research findings suggest that time should be managed after identifying the type of culture—individualistic or collectivist—to decrease the level of stress experienced by university staff. In other words, Hofstede’s cultural dimension has an impact on time management, and organizational stress has a partial mediation effect on this dimension. Although the variables in the study have been studied in the literature together with many different factors, Hofstede is significant in terms of determining the role of organizational stress in the effect of cultural dimensions on time management. The effectiveness of Hofstede’s cultural dimensions through organizational stress in time management allows business and project plans to be carried out in a way that manages individual, team or departmental performances taking into account the organizational stress elements. It is considered that this study will particularly be effective in medicine, project management, and independent auditing.

Open Access Correlational Studies

Adams, R. V., & Blair, E. (2019). Impact of Time Management Behaviors on Undergraduate Engineering Students’ Performance . SAGE Open. https://doi.org/10.1177/2158244018824506

Abstract. Effective time management is associated with greater academic performance and lower levels of anxiety in students; however many students find it hard to find a balance between their studies and their day-to-day lives. This article examines the self-reported time management behaviors of undergraduate engineering students using the Time Management Behavior Scale. Correlation analysis, regression analysis, and model reduction are used to attempt to determine which aspects of time management the students practiced, which time management behaviors were more strongly associated with higher grades within the program, and whether or not those students who self-identified with specific time management behaviors achieved better grades in the program. It was found that students’ perceived control of time was the factor that correlated significantly with cumulative grade point average. On average, it was found that time management behaviors were not significantly different across gender, age, entry qualification, and time already spent in the program.

Cooper, B., & Glaesser, J. (2010). Contrasting Variable-Analytic and Case-Based Approaches to the Analysis of Survey Datasets: Exploring How Achievement Varies by Ability across Configurations of Social Class and Sex. Methodological Innovations Online, 5(1), 3–23. https://doi.org/10.4256/mio.2010.0007

Abstract. The context for this paper is the ongoing debate concerning the relative merits, for the analysis of quantitative data, of, on the one hand, variable-analytic correlational methods, and, on the other, the case-based set theoretic methods developed by Charles Ragin. While correlational approaches, based in linear algebra, typically use regression to establish the net effects of several “independent” variables on an outcome, the set theoretic approach analyses, more holistically, the conjunctions of factors sufficient and/or necessary for an outcome to occur. Here, in order to bring out key differences between the approaches, we focus our attention on the basic building blocks of the two approaches: respectively, the concept of linear correlation and the concept of a sufficient and/or necessary condition. We initially use invented data (for ability, educational achievement, and social class) to simulate what is at stake in this methodological debate and we then employ data taken from the British National Child Development Study to explore the structuring of the relationship between respondents' early measured ability and later educational achievement across various configurations of parental and grandparental class origin and sex. The substantive idea informing the analysis, derived from Boudon's work, is that, for respondents from higher class origins, ability will tend to be sufficient but not necessary for later educational achievement while, for lower class respondents, ability will tend to be necessary but not sufficient. We compare correlational analyses, controlling for class and gender, with fuzzy set analyses to show that set theoretic indices can better capture these varying relationships than correlational measures. In conclusion, we briefly consider how our demonstration of some of the advantages of the set theoretic approach for modelling empirical relationships might be related to the debate concerning the relation between observed regularities and causal mechanisms.

Fang, C., Gai, Q., He, C., & Shi, Q. (2020). The Experience of Poverty Reduction in Rural China. SAGE Open. https://doi.org/10.1177/2158244020982288

Abstract. Since 1978, China has greatly reduced the rural poverty rate. This article provides an overview of the experience of China’s poverty reduction. Using panel data from 1996 to 2013 to calculate farmers’ income dynamics, we found that the pace of poverty reduction was relatively slow from 1996 to 2002 and that the rate of reversion to poverty was high. Since 2003, the pace of poverty reduction has accelerated, whereas the rate of reversion has decreased. Using econometric ordinary least squares and probit models, we explore the factors that drive poverty reduction. We found correlational evidence that the main reasons for poverty reduction in China since 1996 have been the increase in income from household farms and migrant work. In addition, rural public insurance prevented farmers from falling into poverty.

Hayn-Leichsenring, G. U., Lehmann, T., & Redies, C. (2017). Subjective Ratings of Beauty and Aesthetics: Correlations With Statistical Image Properties in Western Oil Paintings . I-Perception. https://doi.org/10.1177/2041669517715474

Abstract. For centuries, oil paintings have been a major segment of the visual arts. The JenAesthetics data set consists of a large number of high-quality images of oil paintings of Western provenance from different art periods. With this database, we studied the relationship between objective image measures and subjective evaluations of the images, especially evaluations on aesthetics (defined as artistic value) and beauty (defined as individual liking). The objective measures represented low-level statistical image properties that have been associated with aesthetic value in previous research. Subjective rating scores on aesthetics and beauty correlated not only with each other but also with different combinations of the objective measures. Furthermore, we found that paintings from different art periods vary with regard to the objective measures, that is, they exhibit specific patterns of statistical image properties. In addition, clusters of participants preferred different combinations of these properties. In conclusion, the results of the present study provide evidence that statistical image properties vary between art periods and subject matters and, in addition, they correlate with the subjective evaluation of paintings by the participants.

Srinivasan P, Rentala S, Kumar P. Impulsivity and Aggression Among Male Delinquent Adolescents Residing in Observation Homes—A Descriptive Correlation Study from East India . Journal of Indian Association for Child and Adolescent Mental Health . 2022;18(4):327-336. doi: 10.1177/09731342231171305

Aggression and crime are connected and highly reported among juveniles in recent times as compared to adults, which ends up in delinquency. It is not just aggression that dominates but the associated impulsiveness also plays a vital role. This study was intended to assess impulsivity and aggression, and their relationship among male delinquent adolescents residing in observation homes. A quantitative research approach with the nonexperimental descriptive correlation design was adopted. One hundred and seventy-nine male delinquent adolescents residing in 2 observation homes in the state of Bihar, India, were selected by convenience sampling technique. The standardized Buss & Perry Aggression questionnaire, and Barratt Impulsiveness scale were used for collecting the data regarding impulsivity and aggression among male delinquent adolescents.

Yamak, O. U., & Eyupoglu, S. Z. (2021). Authentic Leadership and Service Innovative Behavior: Mediating Role of Proactive Personality . SAGE Open. https://doi.org/10.1177/2158244021989629

Abstract. The present study aims to examine the effect of authentic leadership (AL) on service innovative behavior (SIB) of employees as well as to identify whether proactive personality (PP) mediates this connection at an individual level. The quantitative cross-sectional study design was utilized to gather information from a study sample which consisted of 428 front-line employees (FLE) working at banks located in North Cyprus. Specifically, the study uses confirmatory factor analysis (CFA), correlation, structural equation modeling (SEM), and bootstrapping techniques to test the hypothesized relationships. The results reveal that both AL and PP have a significant positive effect on SIB; AL has a positive impact on PP of FLE, and PP plays a partial mediating role between AL and SIB of FLE. By relating the study findings, authenticity and proactivity in the banking sector in North Cyprus play a critical role in fostering the innovative behaviors of FLE. The study also discusses the practical and managerial implications, as well as the future scope.

Books about Quantitative Methods from Sage Publishing

Use the code COMMUNITY3 for a 20% discount when you order her book, valid worldwide until March 31, 2024.

The Research Experience: Planning, Conducting, and Reporting Research by Ann Sloan Devlin (2020). (See Sage Research Methods Community posts by Dr. Devlin.)

Understanding Quantitative Data in Educational Research by Nicoleta Gaciu (2021).

Essentials of Social Statistics for a Diverse Society by Anna Leon-Guerrero, Chava Frankfort-Nachmias, and Georgiann Davis.(2020). (See an author interview for more on this book.)

Understanding Correlation Matrices by Alexandria Hadd and Joseph Lee Rodgers (2021)

Know Your Variables: Little Quick Fix and other titles in the LQF series by John MacInnes

Research Methods for the Behavioral Sciences by Gregory J. Privitera (2022)

Reference Frey, B. (2018). The SAGE encyclopedia of educational research, measurement, and evaluation (Vols. 1-4). Thousand Oaks,, CA: SAGE Publications, Inc. doi: 10.4135/9781506326139

Pros and Cons of Online Survey Research

Research with black participants: scholars rethink methods and methodology.

6.1 Overview of Non-Experimental Research

Learning objectives.

Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).

Most researchers in psychology consider the distinction between experimental and non-experimental research to be an extremely important one. This is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. As we will see, however, this inability to make causal conclusions does not mean that non-experimental research is less important than experimental research.

When to Use Non-Experimental Research

As we saw in the last chapter , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:

the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach will suffice. But the two approaches can also be used to address the same research question in complementary ways. For example, Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into three broad categories: cross-sectional research, correlational research, and observational research.

First, cross-sectional research involves comparing two or more pre-existing groups of people. What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a cross-sectional study because the researcher did not manipulate the students’ nationalities. As another example, if we wanted to compare the memory test performance of a group of cannabis users with a group of non-users, this would be considered a cross-sectional study because for ethical and practical reasons we would not be able to randomly assign participants to the cannabis user and non-user groups. Rather we would need to compare these pre-existing groups which could introduce a selection bias (the groups may differ in other ways that affect their responses on the dependent variable). For instance, cannabis users are more likely to use more alcohol and other drugs and these differences may account for differences in the dependent variable across groups, rather than cannabis use per se.

Cross-sectional designs are commonly used by developmental psychologists who study aging and by researchers interested in sex differences. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect) rather than a direct effect of age. For this reason, longitudinal studies in which one group of people is followed as they age offer a superior means of studying the effects of aging. Once again, cross-sectional designs are also commonly used to study sex differences. Since researchers cannot practically or ethically manipulate the sex of their participants they must rely on cross-sectional designs to compare groups of men and women on different outcomes (e.g., verbal ability, substance use, depression). Using these designs researchers have discovered that men are more likely than women to suffer from substance abuse problems while women are more likely than men to suffer from depression. But, using this design it is unclear what is causing these differences. So, using this design it is unclear whether these differences are due to environmental factors like socialization or biological factors like hormones?

When researchers use a participant characteristic to create groups (nationality, cannabis use, age, sex), the independent variable is usually referred to as an experimenter-selected independent variable (as opposed to the experimenter-manipulated independent variables used in experimental research). Figure 6.1 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a cross-sectional study because it is unclear whether the independent variable was manipulated by the researcher or simply selected by the researcher. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then the independent variable was experimenter-manipulated and it is a true experiment. If the researcher simply asked participants whether they made daily to-do lists or not, then the independent variable it is experimenter-selected and the study is cross-sectional. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a cross-sectional study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead. Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed. The crucial point is that what defines a study as experimental or cross-sectional l is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 6.1 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Second, the most common type of non-experimental research conducted in Psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable. More specifically, in correlational research , the researcher measures two continuous variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related. Correlational research is very similar to cross-sectional research, and sometimes these terms are used interchangeably. The distinction that will be made in this book is that, rather than comparing two or more pre-existing groups of people as is done with cross-sectional research, correlational research involves correlating two continuous variables (groups are not formed and compared).

Third, observational research is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 6.2 shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) is in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Figure 7.1 Internal Validity of Correlational, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlational studies lower still.

Figure 6.2 Internal Validity of Correlation, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlation studies lower still.

Notice also in Figure 6.2 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Key Takeaways

Non-experimental research is research that lacks the manipulation of an independent variable.
There are two broad types of non-experimental research. Correlational research that focuses on statistical relationships between variables that are measured but not manipulated, and observational research in which participants are observed and their behavior is recorded without the researcher interfering or manipulating any variables.
In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.
A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

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Quantitative Research Designs: Non-Experimental vs. Experimental

non experimental quantitative research meaning

While there are many types of quantitative research designs, they generally fall under one of three umbrellas: experimental research, quasi-experimental research, and non-experimental research.

Experimental research designs are what many people think of when they think of research; they typically involve the manipulation of variables and random assignment of participants to conditions. A traditional experiment may involve the comparison of a control group to an experimental group who receives a treatment (i.e., a variable is manipulated). When done correctly, experimental designs can provide evidence for cause and effect. Because of their ability to determine causation, experimental designs are the gold-standard for research in medicine, biology, and so on. However, such designs can also be used in the “soft sciences,” like social science. Experimental research has strict standards for control within the research design and for establishing validity. These designs may also be very resource and labor intensive. Additionally, it can be hard to justify the generalizability of the results in a very tightly controlled or artificial experimental setting. However, if done well, experimental research methods can lead to some very convincing and interesting results.

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Non-experimental research, on the other hand, can be just as interesting, but you cannot draw the same conclusions from it as you can with experimental research. Non-experimental research is usually descriptive or correlational, which means that you are either describing a situation or phenomenon simply as it stands, or you are describing a relationship between two or more variables, all without any interference from the researcher. This means that you do not manipulate any variables (e.g., change the conditions that an experimental group undergoes) or randomly assign participants to a control or treatment group. Without this level of control, you cannot determine any causal effects. While validity is still a concern in non-experimental research, the concerns are more about the validity of the measurements, rather than the validity of the effects.

Finally, a quasi-experimental design is a combination of the two designs described above. For quasi-experimental designs you still can manipulate a variable in the experimental group, but there is no random assignment into groups. Quasi-experimental designs are the most common when the researcher uses a convenience sample to recruit participants. For example, let’s say you were interested in studying the effect of stress on student test scores at the school that you work for. You teach two separate classes so you decide to just use each class as a different group. Class A becomes the experimental group who experiences the stressor manipulation and class B becomes the control group. Because you are sampling from two different pre-existing groups, without any random assignment, this would be known as a quasi-experimental design. These types of designs are very useful for when you want to find a causal relationship between variables but cannot randomly assign people to groups for practical or ethical reasons, such as working with a population of clinically depressed people or looking for gender differences (we can’t randomly assign people to be clinically depressed or to be a different gender). While these types of studies sometimes have higher external validity than a true experimental design, since they involve real world interventions and group rather than a laboratory setting, because of the lack of random assignment in these groups, the generalizability of the study is severely limited.

So, how do you choose between these designs? This will depend on your topic, your available resources, and desired goal. For example, do you want to see if a particular intervention relieves feelings of anxiety? The most convincing results for that would come from a true experimental design with random sampling and random assignment to groups. Ultimately, this is a decision that should be made in close collaboration with your advisor. Therefore, I recommend discussing the pros and cons of each type of research, what it might mean for your personal dissertation process, and what is required of each design before making a decision.

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Non-experimental research: What it is, overview & advantages

Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information.

Unlike experimental research , where the variables are held constant, non-experimental research happens during the study when the researcher cannot control, manipulate or alter the subjects but relies on interpretation or observations to conclude.

This means that the method must not rely on correlations, surveys , or case studies and cannot demonstrate an actual cause and effect relationship.

Characteristics of non-experimental research

Some of the essential characteristics of non-experimental research are necessary for the final results. Let’s talk about them to identify the most critical parts of them.

Most studies are based on events that occurred previously and are analyzed later.
In this method, controlled experiments are not performed for reasons such as ethics or morality.
No study samples are created; on the contrary, the samples or participants already exist and develop in their environment.
The researcher does not intervene directly in the environment of the sample.
This method studies the phenomena exactly as they occurred.

Types of non-experimental research

Non-experimental research can take the following forms:

Cross-sectional research : Cross-sectional research is used to observe and analyze the exact time of the research to cover various study groups or samples. This type of research is divided into:

Descriptive: When values are observed where one or more variables are presented.
Causal: It is responsible for explaining the reasons and relationship that exists between variables in a given time.

Longitudinal research: In a longitudinal study , researchers aim to analyze the changes and development of the relationships between variables over time. Longitudinal research can be divided into:

Trend: When they study the changes faced by the study group in general.
Group evolution: When the study group is a smaller sample.
Panel: It is in charge of analyzing individual and group changes to discover the factor that produces them.

LEARN ABOUT: Quasi-experimental Research

When to use non-experimental research

Non-experimental research can be applied in the following ways:

When the research question may be about one variable rather than a statistical relationship about two variables.
There is a non-causal statistical relationship between variables in the research question.
The research question has a causal research relationship, but the independent variable cannot be manipulated.
In exploratory or broad research where a particular experience is confronted.

Advantages and disadvantages

Some advantages of non-experimental research are:

It is very flexible during the research process
The cause of the phenomenon is known, and the effect it has is investigated.
The researcher can define the characteristics of the study group.

Among the disadvantages of non-experimental research are:

The groups are not representative of the entire population.
Errors in the methodology may occur, leading to research biases .

Non-experimental research is based on the observation of phenomena in their natural environment. In this way, they can be studied later to reach a conclusion.

Difference between experimental and non-experimental research

Experimental research involves changing variables and randomly assigning conditions to participants. As it can determine the cause, experimental research designs are used for research in medicine, biology, and social science.

Experimental research designs have strict standards for control and establishing validity. Although they may need many resources, they can lead to very interesting results.

Non-experimental research, on the other hand, is usually descriptive or correlational without any explicit changes done by the researcher. You simply describe the situation as it is, or describe a relationship between variables. Without any control, it is difficult to determine causal effects. The validity remains a concern in this type of research. However, it’s’ more regarding the measurements instead of the effects.

LEARN MORE: Descriptive Research vs Correlational Research

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7.1 Overview of Nonexperimental Research

Learning objectives.

Define nonexperimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct nonexperimental research as opposed to experimental research.

What Is Nonexperimental Research?

Nonexperimental research is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are not . But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This is because while experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense.

When to Use Nonexperimental Research

As we saw in Chapter 6 “Experimental Research” , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable and randomly assign participants to conditions or to orders of conditions. It stands to reason, therefore, that nonexperimental research is appropriate—even necessary—when these conditions are not met. There are many ways in which this can be the case.

The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?).
The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?).
The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and nonexperimental approaches is generally dictated by the nature of the research question. If it is about a causal relationship and involves an independent variable that can be manipulated, the experimental approach is typically preferred. Otherwise, the nonexperimental approach is preferred. But the two approaches can also be used to address the same research question in complementary ways. For example, nonexperimental studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001). Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974).

Types of Nonexperimental Research

Nonexperimental research falls into three broad categories: single-variable research, correlational and quasi-experimental research, and qualitative research. First, research can be nonexperimental because it focuses on a single variable rather than a statistical relationship between two variables. Although there is no widely shared term for this kind of research, we will call it single-variable research . Milgram’s original obedience study was nonexperimental in this way. He was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of single-variable research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the research asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.)

As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their self-esteem. But this would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied compares with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied.

Research can also be nonexperimental because it focuses on a statistical relationship between two variables but does not include the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both. This kind of research takes two basic forms: correlational research and quasi-experimental research. In correlational research , the researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them. A research methods student who finds out whether each of several middle-school students has been bullied and then measures each student’s self-esteem is conducting correlational research. In quasi-experimental research , the researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions. For example, a researcher might start an antibullying program (a kind of treatment) at one school and compare the incidence of bullying at that school with the incidence at a similar school that has no antibullying program.

The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In qualitative research , the data are usually nonnumerical and are analyzed using nonstatistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256).

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 7.1 shows how experimental, quasi-experimental, and correlational research vary in terms of internal validity. Experimental research tends to be highest because it addresses the directionality and third-variable problems through manipulation and the control of extraneous variables through random assignment. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Correlational research is lowest because it fails to address either problem. If the average score on the dependent variable differs across levels of the independent variable, it could be that the independent variable is responsible, but there are other interpretations. In some situations, the direction of causality could be reversed. In others, there could be a third variable that is causing differences in both the independent and dependent variables. Quasi-experimental research is in the middle because the manipulation of the independent variable addresses some problems, but the lack of random assignment and experimental control fails to address others. Imagine, for example, that a researcher finds two similar schools, starts an antibullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” There is no directionality problem because clearly the number of bullying incidents did not determine which school got the program. However, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying.

Experiments are generally high in internal validity, quasi-experiments lower, and correlational studies lower still.

Notice also in Figure 7.1 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well designed quasi-experiment with no obvious confounding variables.

Key Takeaways

Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both.
There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables. Correlational and quasi-experimental research focus on a statistical relationship but lack manipulation or random assignment. Qualitative research focuses on broader research questions, typically involves collecting large amounts of data from a small number of participants, and analyzes the data nonstatistically.
In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.

Discussion: For each of the following studies, decide which type of research design it is and explain why.

A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.

Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage.

Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row.

Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 7: Nonexperimental Research

Overview of Nonexperimental Research

Learning Objectives

Define nonexperimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct nonexperimental research as opposed to experimental research.

What Is Nonexperimental Research?

Nonexperimental research is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are not . But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This distinction is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this inability does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense.

When to Use Nonexperimental Research

As we saw in Chapter 6 , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable and randomly assign participants to conditions or to orders of conditions. It stands to reason, therefore, that nonexperimental research is appropriate—even necessary—when these conditions are not met. There are many ways in which preferring nonexperimental research can be the case.

The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?).
The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?).
The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and nonexperimental approaches is generally dictated by the nature of the research question. If it is about a causal relationship and involves an independent variable that can be manipulated, the experimental approach is typically preferred. Otherwise, the nonexperimental approach is preferred. But the two approaches can also be used to address the same research question in complementary ways. For example, nonexperimental studies establishing that there is a relationship between watching violent television and aggressive behaviour have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] . Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [2] .

Types of Nonexperimental Research

As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This detail is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their self-esteem. But this design would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied compares with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied thereby introducing another variable.

The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256). [3] Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 7.1 shows how experimental, quasi-experimental, and correlational research vary in terms of internal validity. Experimental research tends to be highest because it addresses the directionality and third-variable problems through manipulation and the control of extraneous variables through random assignment. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Correlational research is lowest because it fails to address either problem. If the average score on the dependent variable differs across levels of the independent variable, it could be that the independent variable is responsible, but there are other interpretations. In some situations, the direction of causality could be reversed. In others, there could be a third variable that is causing differences in both the independent and dependent variables. Quasi-experimental research is in the middle because the manipulation of the independent variable addresses some problems, but the lack of random assignment and experimental control fails to address others. Imagine, for example, that a researcher finds two similar schools, starts an antibullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” There is no directionality problem because clearly the number of bullying incidents did not determine which school got the program. However, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying.

Key Takeaways

Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both.
There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables. Correlational and quasi-experimental research focus on a statistical relationship but lack manipulation or random assignment. Qualitative research focuses on broader research questions, typically involves collecting large amounts of data from a small number of participants, and analyses the data nonstatistically.
In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.

Discussion: For each of the following studies, decide which type of research design it is and explain why.

A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

Research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

Research that focuses on a single variable rather than a statistical relationship between two variables.

The researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them.

The researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is non-experimental research: Definition, types & examples

What is non-experimental research: Definition, types & examples

Defne Çobanoğlu

The experimentation method is very useful for getting information on a specific subject. However, when experimenting is not possible or practical, there is another way of collecting data for those interested. It's a non-experimental way, to say the least.

In this article, we have gathered information on non-experimental research, clearly defined what it is and when one should use it, and listed the types of non-experimental research. We also gave some useful examples to paint a better picture. Let us get started.

What is non-experimental research?

Non-experimental research is a type of research design that is based on observation and measuring instead of experimentation with randomly assigned participants.

What characterizes this research design is the fact that it lacks the manipulation of independent variables . Because of this fact, the non-experimental research is based on naturally occurring conditions, and there is no involvement of external interventions. Therefore, the researchers doing this method must not rely heavily on interviews, surveys , or case studies.

When to use non-experimental research?

An experiment is done when a researcher is investigating the relationship between one or two phenomena and has a theory or hypothesis on the relationship between two variables that are involved. The researcher can carry out an experiment when it is ethical, possible, and feasible to do one.

However, when an experiment can not be done because of a limitation, then they decide to opt for a non-experimental research design . Non-experimental research is considered preferable in some conditions, including:

When the manipulation of the independent variable is not possible because of ethical or practical concerns
When the subjects of an experimental design can not be randomly assigned to treatments.
When the research question is too extensive or it relates to a general experience.
When researchers want to do a starter research before investing in more extensive research.
When the research question is about the statistical relationship between variables , but in a noncausal context.
Characteristics of non-experimental research

Non-experimental research has some characteristics that clearly define the framework of this research method. They provide a clear distinction between experimental design and non-experimental design. Let us see some of them:

Non-experimental research does not involve the manipulation of variables .
The aim of this research type is to explore the factors as they naturally occur .
This method is used when experimentation is not possible because of ethical or practical reasons .
Instead of creating a sample or participant group, the existing groups or natural thresholds are used during the research.
This research method is not about finding causality between two variables.
Most studies are done on past events or historical occurrences to make sense of specific research questions.
Types of non-experimental research

Non-experimental research types

What makes research non-experimental research is the fact that the researcher does not manipulate the factors, does not randomly assign the participants, and observes the existing groups. But this research method can also be divided into different types. These types are:

Correlational research:

In correlation studies, the researcher does not manipulate the variables and is not interested in controlling the extraneous variables. They only observe and assess the relationship between them. For example, a researcher examines students’ study hours every day and their overall academic performance. The positive correlation this between study hours and academic performance suggests a statistical association.

Quasi-experimental research:

In quasi-experimental research, the researcher does not randomly assign the participants into two groups. Because you can not deliberately deprive someone of treatment, the researcher uses natural thresholds or dividing points . For example, examining students from two different high schools with different education methods.

Cross-sectional research:

In cross-sectional research, the researcher studies and compares a portion of a population at the same time . It does not involve random assignment or any outside manipulation. For example, a study on smokers and non-smokers in a specific area.

Observational research:

In observational research, the researcher once again does not manipulate any aspect of the study, and their main focus is observation of the participants . For example, a researcher examining a group of children playing in a playground would be a good example.

Non-experimental research examples

Non-experimental research is a good way of collecting information and exploring relationships between variables. It can be used in numerous fields, from social sciences, economics, psychology, education, and market research. When gathering information using secondary research is not enough and an experiment can not be done, this method can bring out new information.

Non-experimental research example #1

Imagine a researcher who wants to see the connection between mobile phone usage before bedtime and the amount of sleep adults get in a night . They can gather a group of individuals to observe and present them with some questions asking about the details of their day, frequency and duration of phone usage, quality of sleep, etc . And observe them by analyzing the findings.

Non-experimental research example #2

Imagine a researcher who wants to explore the correlation between job satisfaction levels among employees and what are the factors that affect this . The researcher can gather all the information they get about the employees’ ages, sexes, positions in the company, working patterns, demographic information, etc .

The research provides the researcher with all the information to make an analysis to identify correlations and patterns. Then, it is possible for researchers and administrators to make informed predictions.

Frequently asked questions about non-experimental research

When not to use non-experimental research?

There are some situations where non-experimental research is not suitable or the best choice. For example, the aim of non-experimental research is not about finding causality therefore, if the researcher wants to explore the relationship between two variables, then this method is not for them. Also, if the control over the variables is extremely important to the test of a theory, then experimentation is a more appropriate option.

What is the difference between experimental and non-experimental research?

Experimental research is an example of primary research where the researcher takes control of all the variables, randomly assigns the participants into different groups, and studies them in a pre-determined environment to test a hypothesis.

On the contrary, non-experimental research does not intervene in any way and only observes and studies the participants in their natural environments to make sense of a phenomenon

What makes a quasi-experiment a non-experiment?

The same as true experimentation, quasi-experiment research also aims to explore a cause-and-effect relationship between independent and dependent variables. However, in quasi-experimental research, the participants are not randomly selected. They are assigned to groups based on non-random criteria .

Is a survey a non-experimental study?

Yes, as the main purpose of a survey or questionnaire is to collect information from participants without outside interference, it makes the survey a non-experimental study. Surveys are used by researchers when experimentation is not possible because of ethical reasons, but first-hand data is needed

What is non-experimental data?

Non-experimental data is data collected by researchers via using non-experimental methods such as observations, interpretation, and interactions. Non-experimental data could both be qualitative or quantitative, depending on the situation.

Advantages of non-experimental research

Non-experimental research has its positive sides that a researcher should have in mind when going through a study. They can start their research by going through the advantages. These advantages are:

It is used to observe and analyze past events .
This method is more affordable than a true experiment .
As the researcher can adapt the methods during the study, this research type is more flexible than an experimental study.
This method allows the researchers to answer specific questions .

Disadvantages of non-experimental research

Even though non-experimental research has its advantages, it also has some disadvantages a researcher should be mindful of. Here are some of them:

The findings of non-experimental research can not be generalized to the whole population. Therefore, it has low external validity .
This research is used to explore only a single variable .
Non-experimental research designs are prone to researcher bias and may not produce neutral results.
Final words

A non-experimental study differs from an experimental study in that there is no intervention or change of internal or extraneous elements. It is a smart way to collect information without the limitations of experimentation. These limitations could be about ethical or practical problems. When you can not do proper experimentation, your other option is to study existing conditions and groups to draw conclusions. This is a non-experimental design .

In this article, we have gathered information on non-experimental research to shed light on the details of this research method. If you are thinking of doing a study, make sure to have this information in mind. And lastly, do not forget to visit our articles on other research methods and so much more!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

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Non-Experimental Research

28 Overview of Non-Experimental Research

Learning objectives.

Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

When to Use Non-Experimental Research

the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., how accurate are people’s first impressions?).
the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach is appropriate. But the two approaches can also be used to address the same research question in complementary ways. For example, in Milgram’s original (non-experimental) obedience study, he was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. However, Milgram subsequently conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into two broad categories: correlational research and observational research.

The most common type of non-experimental research conducted in psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable. More specifically, in correlational research , the researcher measures two variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related.

Observational research is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories).

Cross-Sectional, Longitudinal, and Cross-Sequential Studies

When psychologists wish to study change over time (for example, when developmental psychologists wish to study aging) they usually take one of three non-experimental approaches: cross-sectional, longitudinal, or cross-sequential. Cross-sectional studies involve comparing two or more pre-existing groups of people (e.g., children at different stages of development). What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect ) rather than a direct effect of age. For this reason, longitudinal studies , in which one group of people is followed over time as they age, offer a superior means of studying the effects of aging. However, longitudinal studies are by definition more time consuming and so require a much greater investment on the part of the researcher and the participants. A third approach, known as cross-sequential studies , combines elements of both cross-sectional and longitudinal studies. Rather than measuring differences between people in different age groups or following the same people over a long period of time, researchers adopting this approach choose a smaller period of time during which they follow people in different age groups. For example, they might measure changes over a ten year period among participants who at the start of the study fall into the following age groups: 20 years old, 30 years old, 40 years old, 50 years old, and 60 years old. This design is advantageous because the researcher reaps the immediate benefits of being able to compare the age groups after the first assessment. Further, by following the different age groups over time they can subsequently determine whether the original differences they found across the age groups are due to true age effects or cohort effects.

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in psychiatric wards was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 6.1 shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) falls in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the inability to randomly assign children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Figure 6.1 Internal Validity of Correlational, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlational studies lower still.

Notice also in Figure 6.1 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational (non-experimental) studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

A research that lacks the manipulation of an independent variable.

Research that is non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

Studies that involve comparing two or more pre-existing groups of people (e.g., children at different stages of development).

Differences between the groups may reflect the generation that people come from rather than a direct effect of age.

Studies in which one group of people are followed over time as they age.

Studies in which researchers follow people in different age groups in a smaller period of time.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Non-Experimental Research: Overview & Advantages

In traditional experimental research, variables are carefully controlled by researchers in a lab setting. In non-experimental study, there are no variables the observer can directly control.

Non-Experimental Research

Non-experimental research gets its name from the fact that there is no independent variable involved in testing. Researchers instead look to take past events and re-examine them; analyzing them for new information and coming to new or supporting conclusions.

In traditional experimental research, variables are carefully controlled by researchers in a lab setting. In non-experimental study, there are no variables the observer can directly control. Instead, researchers are tasked with parsing through established context to come up with their own interpretation of the events. While non-experimental research is limited in use, there are a few key areas where a researcher may find using this kind of methodology is beneficial.

Characteristics of Non-Experimental Research

These key characteristics of non-experimental research set it apart from other common methods:

The vast majority of these studies are conducted using prior events and past experiences.
This method is not concerned with establishing links between variables.
The research collected does not directly influence the events that are being studied.
This type of testing does not influence or impact the phenomena being studied.

Types of Non-Experimental Research

There are three primary forms of non-experimental research. They are:

Single-Variable Research

Single-variable research involves locating one variable and attempting to discern new meaning from these events. Instead of trying to discern a relationship between two variables, this type of study aims to ganer a deeper understanding of a particular issue - often so that further testing can be completed.

One example of a single-variable research project could involve looking at how high the average person can jump. In this case, researchers would invite participants to make 3 attempts to jump up into the air as high as they could from a standing position; researchers would then average out the 3 attempts into one number. In this case, researchers are not looking to connect the variable jump height with any other piece of information. All the study is concerned about is measuring the average of an individual’s jumps.

Correlational and Quasi-Experimental

Correlational research involves measuring two or more variables of interest while maintaining little or no control over the variables themselves. In the quasi-experimental method, researchers change an independent variable - but will not recruit or control the participants involved in the experiment. An example would be a researcher who starts a campaign urging people to stop smoking in one city - and then comparing those results to cities without a no-smoking program.

Qualitative Research

The qualitative research method seeks to answer complex questions, and involves written documentation of experiences and events. Unlike the quantitative research method, which is concerned with facts and hard data, the qualitative method can be used to gather insights for a breadth of vital topics.

Advantages of Non-Experimental Research

Non-experimental designs can open a number of advantageous research opportunities. The benefits include:

Non-experimental research can be used to analyze events that have happened in the past.
The versatility of the model can be used to observe many unique phenomena.
This method of research is far more affordable than the experimental kind.

Disadvantages of Non-Experimental Research

The limitations of non-experimental research are:

These limited samples do not represent the larger population.
The research can only be used to observe a single variable.
Researcher bias or error in the methodology can lead to inaccurate results.

These disadvantages can be mitigated by applying the non-experimental method to the correct situations.

Disadvantages of Non-Experimental Research

How it is different from experimental research

Experimental research often involves taking two or more variables (independent and dependent) and attempting to develop a causal relationship between them. Experimental designs will be tightly controlled by researchers, and the tests themselves will often be far more intricate and expansive than non-experimental ones.

When to use Non-Experimental Research

Non-experimental research is best suited for situations where you want to observe events that have already happened; or you are only interested in gathering information about one isolated variable.

Experimental designs are far more common in the fields of science: medicine, biology, psychology, and so forth. Non-experimental design often sees use in business, politics, history, and general academia.

Determining when you should use either experimental or non-experimental methods boil down to the purpose of your research.

If the situation calls for direct intervention, then experimental methods offer researchers more tools for changing and measuring independent variables.

The best place to use non-experimental research design is when the question at hand can be answered without altering the independent variable.

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Experimental Vs Non-Experimental Research: 15 Key Differences

There is a general misconception around research that once the research is non-experimental, then it is non-scientific, making it more important to understand what experimental and experimental research entails. Experimental research is the most common type of research, which a lot of people refer to as scientific research.

Non experimental research, on the other hand, is easily used to classify research that is not experimental. It clearly differs from experimental research, and as such has different use cases.

In this article, we will be explaining these differences in detail so as to ensure proper identification during the research process.

What is Experimental Research?

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables of the research subject(s) and measuring the effect of this manipulation on the subject. It is known for the fact that it allows the manipulation of control variables.

This research method is widely used in various physical and social science fields, even though it may be quite difficult to execute. Within the information field, they are much more common in information systems research than in library and information management research.

Experimental research is usually undertaken when the goal of the research is to trace cause-and-effect relationships between defined variables. However, the type of experimental research chosen has a significant influence on the results of the experiment.

Therefore bringing us to the different types of experimental research. There are 3 main types of experimental research, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research

Pre-experimental research is the simplest form of research, and is carried out by observing a group or groups of dependent variables after the treatment of an independent variable which is presumed to cause change on the group(s). It is further divided into three types.

One-shot case study research
One-group pretest-posttest research
Static-group comparison

Quasi-experimental Research

The Quasi type of experimental research is similar to true experimental research, but uses carefully selected rather than randomized subjects. The following are examples of quasi-experimental research:

Time series
No equivalent control group design
Counterbalanced design.

True Experimental Research

True experimental research is the most accurate type, and may simply be called experimental research. It manipulates a control group towards a group of randomly selected subjects and records the effect of this manipulation.

True experimental research can be further classified into the following groups:

The posttest-only control group
The pretest-posttest control group
Solomon four-group

Pros of True Experimental Research

Researchers can have control over variables.
It can be combined with other research methods.
The research process is usually well structured.
It provides specific conclusions.
The results of experimental research can be easily duplicated.

Cons of True Experimental Research

It is highly prone to human error.
Exerting control over extraneous variables may lead to the personal bias of the researcher.
It is time-consuming.
It is expensive.
Manipulating control variables may have ethical implications.
It produces artificial results.

What is Non-Experimental Research?

Non-experimental research is the type of research that does not involve the manipulation of control or independent variable. In non-experimental research, researchers measure variables as they naturally occur without any further manipulation.

This type of research is used when the researcher has no specific research question about a causal relationship between 2 different variables, and manipulation of the independent variable is impossible. They are also used when:

subjects cannot be randomly assigned to conditions.
the research subject is about a causal relationship but the independent variable cannot be manipulated.
the research is broad and exploratory
the research pertains to a non-causal relationship between variables.
limited information can be accessed about the research subject.

There are 3 main types of non-experimental research , namely; cross-sectional research, correlation research, and observational research.

Cross-sectional Research

Cross-sectional research involves the comparison of two or more pre-existing groups of people under the same criteria. This approach is classified as non-experimental because the groups are not randomly selected and the independent variable is not manipulated.

For example, an academic institution may want to reward its first-class students with a scholarship for their academic excellence. Therefore, each faculty places students in the eligible and ineligible group according to their class of degree.

In this case, the student’s class of degree cannot be manipulated to qualify him or her for a scholarship because it is an unethical thing to do. Therefore, the placement is cross-sectional.

Correlational Research

Correlational type of research compares the statistical relationship between two variables .Correlational research is classified as non-experimental because it does not manipulate the independent variables.

For example, a researcher may wish to investigate the relationship between the class of family students come from and their grades in school. A questionnaire may be given to students to know the average income of their family, then compare it with CGPAs.

The researcher will discover whether these two factors are positively correlated, negatively corrected, or have zero correlation at the end of the research.

Observational Research

Observational research focuses on observing the behavior of a research subject in a natural or laboratory setting. It is classified as non-experimental because it does not involve the manipulation of independent variables.

A good example of observational research is an investigation of the crowd effect or psychology in a particular group of people. Imagine a situation where there are 2 ATMs at a place, and only one of the ATMs is filled with a queue, while the other is abandoned.

The crowd effect infers that the majority of newcomers will also abandon the other ATM.

You will notice that each of these non-experimental research is descriptive in nature. It then suffices to say that descriptive research is an example of non-experimental research.

Pros of Observational Research

The research process is very close to a real-life situation.
It does not allow for the manipulation of variables due to ethical reasons.
Human characteristics are not subject to experimental manipulation.

Cons of Observational Research

The groups may be dissimilar and nonhomogeneous because they are not randomly selected, affecting the authenticity and generalizability of the study results.
The results obtained cannot be absolutely clear and error-free.

What Are The Differences Between Experimental and Non-Experimental Research?

Definitions

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables and measuring their defect on the dependent variables, while non-experimental research is the type of research that does not involve the manipulation of control variables.

The main distinction in these 2 types of research is their attitude towards the manipulation of control variables. Experimental allows for the manipulation of control variables while non-experimental research doesn’t.

Examples of experimental research are laboratory experiments that involve mixing different chemical elements together to see the effect of one element on the other while non-experimental research examples are investigations into the characteristics of different chemical elements.

Consider a researcher carrying out a laboratory test to determine the effect of adding Nitrogen gas to Hydrogen gas. It may be discovered that using the Haber process, one can create Nitrogen gas.

Non-experimental research may further be carried out on Ammonia, to determine its characteristics, behaviour, and nature.

There are 3 types of experimental research, namely; experimental research, quasi-experimental research, and true experimental research. Although also 3 in number, non-experimental research can be classified into cross-sectional research, correlational research, and observational research.

The different types of experimental research are further divided into different parts, while non-experimental research types are not further divided. Clearly, these divisions are not the same in experimental and non-experimental research.

Characteristics

Experimental research is usually quantitative, controlled, and multivariable. Non-experimental research can be both quantitative and qualitative , has an uncontrolled variable, and also a cross-sectional research problem.

The characteristics of experimental research are the direct opposite of that of non-experimental research. The most distinct characteristic element is the ability to control or manipulate independent variables in experimental research and not in non-experimental research.

In experimental research, a level of control is usually exerted on extraneous variables, therefore tampering with the natural research setting. Experimental research settings are usually more natural with no tampering with the extraneous variables.

Data Collection/Tools

The data used during experimental research is collected through observational study, simulations, and surveys while non-experimental data is collected through observations, surveys, and case studies. The main distinction between these data collection tools is case studies and simulations.

Even at that, similar tools are used differently. For example, an observational study may be used during a laboratory experiment that tests how the effect of a control variable manifests over a period of time in experimental research.

However, when used in non-experimental research, data is collected based on the researcher’s discretion and not through a clear scientific reaction. In this case, we see a difference in the level of objectivity.

The goal of experimental research is to measure the causes and effects of variables present in research, while non-experimental research provides very little to no information about causal agents.

Experimental research answers the question of why something is happening. This is quite different in non-experimental research, as they are more descriptive in nature with the end goal being to describe what .

Experimental research is mostly used to make scientific innovations and find major solutions to problems while non-experimental research is used to define subject characteristics, measure data trends, compare situations and validate existing conditions.

For example, if experimental research results in an innovative discovery or solution, non-experimental research will be conducted to validate this discovery. This research is done for a period of time in order to properly study the subject of research.

Experimental research process is usually well structured and as such produces results with very little to no errors, while non-experimental research helps to create real-life related experiments. There are a lot more advantages of experimental and non-experimental research , with the absence of each of these advantages in the other leaving it at a disadvantage.

For example, the lack of a random selection process in non-experimental research leads to the inability to arrive at a generalizable result. Similarly, the ability to manipulate control variables in experimental research may lead to the personal bias of the researcher.

Disadvantage

Experimental research is highly prone to human error while the major disadvantage of non-experimental research is that the results obtained cannot be absolutely clear and error-free. In the long run, the error obtained due to human error may affect the results of the experimental research.

Some other disadvantages of experimental research include the following; extraneous variables cannot always be controlled, human responses can be difficult to measure, and participants may also cause bias.

In experimental research, researchers can control and manipulate control variables, while in non-experimental research, researchers cannot manipulate these variables. This cannot be done due to ethical reasons.

For example, when promoting employees due to how well they did in their annual performance review, it will be unethical to manipulate the results of the performance review (independent variable). That way, we can get impartial results of those who deserve a promotion and those who don’t.

Experimental researchers may also decide to eliminate extraneous variables so as to have enough control over the research process. Once again, this is something that cannot be done in non-experimental research because it relates more to real-life situations.

Experimental research is carried out in an unnatural setting because most of the factors that influence the setting are controlled while the non-experimental research setting remains natural and uncontrolled. One of the things usually tampered with during research is extraneous variables.

In a bid to get a perfect and well-structured research process and results, researchers sometimes eliminate extraneous variables. Although sometimes seen as insignificant, the elimination of these variables may affect the research results.

Consider the optimization problem whose aim is to minimize the cost of production of a car, with the constraints being the number of workers and the number of hours they spend working per day.

In this problem, extraneous variables like machine failure rates or accidents are eliminated. In the long run, these things may occur and may invalidate the result.

Cause-Effect Relationship

The relationship between cause and effect is established in experimental research while it cannot be established in non-experimental research. Rather than establish a cause-effect relationship, non-experimental research focuses on providing descriptive results.

Although it acknowledges the causal variable and its effect on the dependent variables, it does not measure how or the extent to which these dependent variables change. It, however, observes these changes, compares the changes in 2 variables, and describes them.

Experimental research does not compare variables while non-experimental research does. It compares 2 variables and describes the relationship between them.

The relationship between these variables can be positively correlated, negatively correlated or not correlated at all. For example, consider a case whereby the subject of research is a drum, and the control or independent variable is the drumstick.

Experimental research will measure the effect of hitting the drumstick on the drum, where the result of this research will be sound. That is, when you hit a drumstick on a drum, it makes a sound.

Non-experimental research, on the other hand, will investigate the correlation between how hard the drum is hit and the loudness of the sound that comes out. That is, if the sound will be higher with a harder bang, lower with a harder bang, or will remain the same no matter how hard we hit the drum.

Quantitativeness

Experimental research is a quantitative research method while non-experimental research can be both quantitative and qualitative depending on the time and the situation where it is been used. An example of a non-experimental quantitative research method is correlational research .

Researchers use it to correlate two or more variables using mathematical analysis methods. The original patterns, relationships, and trends between variables are observed, then the impact of one of these variables on the other is recorded along with how it changes the relationship between the two variables.

Observational research is an example of non-experimental research, which is classified as a qualitative research method.

Cross-section

Experimental research is usually single-sectional while non-experimental research is cross-sectional. That is, when evaluating the research subjects in experimental research, each group is evaluated as an entity.

For example, let us consider a medical research process investigating the prevalence of breast cancer in a certain community. In this community, we will find people of different ages, ethnicities, and social backgrounds.

If a significant amount of women from a particular age are found to be more prone to have the disease, the researcher can conduct further studies to understand the reason behind it. A further study into this will be experimental and the subject won’t be a cross-sectional group.

A lot of researchers consider the distinction between experimental and non-experimental research to be an extremely important one. This is partly due to the fact that experimental research can accommodate the manipulation of independent variables, which is something non-experimental research can not.

Therefore, as a researcher who is interested in using any one of experimental and non-experimental research, it is important to understand the distinction between these two. This helps in deciding which method is better for carrying out particular research.

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Please note you do not have access to teaching notes, nonexperimental research: strengths, weaknesses and issues of precision.

European Journal of Training and Development

ISSN : 2046-9012

Article publication date: 6 September 2016

Nonexperimental research, defined as any kind of quantitative or qualitative research that is not an experiment, is the predominate kind of research design used in the social sciences. How to unambiguously and correctly present the results of nonexperimental research, however, remains decidedly unclear and possibly detrimental to applied disciplines such as human resource development. To clarify issues about the accurate reporting and generalization of nonexperimental research results, this paper aims to present information about the relative strength of research designs, followed by the strengths and weaknesses of nonexperimental research. Further, some possible ways to more precisely report nonexperimental findings without using causal language are explored. Next, the researcher takes the position that the results of nonexperimental research can be used cautiously, yet appropriately, for making practice recommendations. Finally, some closing thoughts about nonexperimental research and the appropriate use of causal language are presented.

Design/methodology/approach

A review of the extant social science literature was consulted to inform this paper.

Nonexperimental research, when reported accurately, makes a tremendous contribution because it can be used for conducting research when experimentation is not feasible or desired. It can be used also to make tentative recommendations for practice.

Originality/value

This article presents useful means to more accurately report nonexperimental findings through avoiding causal language. Ways to link nonexperimental results to making practice recommendations are explored.

Research design
Experimental design
Causal inference
Nonexperimental
Social science research
Triangulation

Reio, T.G. (2016), "Nonexperimental research: strengths, weaknesses and issues of precision", European Journal of Training and Development , Vol. 40 No. 8/9, pp. 676-690. https://doi.org/10.1108/EJTD-07-2015-0058

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What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

In descriptive research , you simply seek an overall summary of your study variables.
In correlational research , you investigate relationships between your study variables.
In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Quantitative research methods
Research method	How to use	Example
	Control or manipulate an to measure its effect on a dependent variable.	To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention.
	Ask questions of a group of people in-person, over-the-phone or online.	You distribute with rating scales to first-year international college students to investigate their experiences of culture shock.
(Systematic) observation	Identify a behavior or occurrence of interest and monitor it in its natural setting.	To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds.
Secondary research	Collect data that has been gathered for other purposes e.g., national surveys or historical records.	To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available .

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Prevent plagiarism. Run a free check.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Chi square goodness of fit test
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Inclusion and exclusion criteria

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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2.5: Experimental and Non-experimental Research

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Danielle Navarro
University of New South Wales

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One of the big distinctions that you should be aware of is the distinction between “experimental research” and “non-experimental research”. When we make this distinction, what we’re really talking about is the degree of control that the researcher exercises over the people and events in the study.

Experimental research

The key features of experimental research is that the researcher controls all aspects of the study, especially what participants experience during the study. In particular, the researcher manipulates or varies the predictor variables (IVs), and then allows the outcome variable (DV) to vary naturally. The idea here is to deliberately vary the predictors (IVs) to see if they have any causal effects on the outcomes. Moreover, in order to ensure that there’s no chance that something other than the predictor variables is causing the outcomes, everything else is kept constant or is in some other way “balanced” to ensure that they have no effect on the results. In practice, it’s almost impossible to think of everything else that might have an influence on the outcome of an experiment, much less keep it constant. The standard solution to this is randomisation: that is, we randomly assign people to different groups, and then give each group a different treatment (i.e., assign them different values of the predictor variables). We’ll talk more about randomisation later in this course, but for now, it’s enough to say that what randomisation does is minimise (but not eliminate) the chances that there are any systematic difference between groups.

Let’s consider a very simple, completely unrealistic and grossly unethical example. Suppose you wanted to find out if smoking causes lung cancer. One way to do this would be to find people who smoke and people who don’t smoke, and look to see if smokers have a higher rate of lung cancer. This is not a proper experiment, since the researcher doesn’t have a lot of control over who is and isn’t a smoker. And this really matters: for instance, it might be that people who choose to smoke cigarettes also tend to have poor diets, or maybe they tend to work in asbestos mines, or whatever. The point here is that the groups (smokers and non-smokers) actually differ on lots of things, not just smoking. So it might be that the higher incidence of lung cancer among smokers is caused by something else, not by smoking per se. In technical terms, these other things (e.g. diet) are called “confounds”, and we’ll talk about those in just a moment.

In the meantime, let’s now consider what a proper experiment might look like. Recall that our concern was that smokers and non-smokers might differ in lots of ways. The solution, as long as you have no ethics, is to control who smokes and who doesn’t. Specifically, if we randomly divide participants into two groups, and force half of them to become smokers, then it’s very unlikely that the groups will differ in any respect other than the fact that half of them smoke. That way, if our smoking group gets cancer at a higher rate than the non-smoking group, then we can feel pretty confident that (a) smoking does cause cancer and (b) we’re murderers.

Non-experimental research

Non-experimental research is a broad term that covers “any study in which the researcher doesn’t have quite as much control as they do in an experiment”. Obviously, control is something that scientists like to have, but as the previous example illustrates, there are lots of situations in which you can’t or shouldn’t try to obtain that control. Since it’s grossly unethical (and almost certainly criminal) to force people to smoke in order to find out if they get cancer, this is a good example of a situation in which you really shouldn’t try to obtain experimental control. But there are other reasons too. Even leaving aside the ethical issues, our “smoking experiment” does have a few other issues. For instance, when I suggested that we “force” half of the people to become smokers, I must have been talking about starting with a sample of non-smokers, and then forcing them to become smokers. While this sounds like the kind of solid, evil experimental design that a mad scientist would love, it might not be a very sound way of investigating the effect in the real world. For instance, suppose that smoking only causes lung cancer when people have poor diets, and suppose also that people who normally smoke do tend to have poor diets. However, since the “smokers” in our experiment aren’t “natural” smokers (i.e., we forced non-smokers to become smokers; they didn’t take on all of the other normal, real life characteristics that smokers might tend to possess) they probably have better diets. As such, in this silly example they wouldn’t get lung cancer, and our experiment will fail, because it violates the structure of the “natural” world (the technical name for this is an “artifactual” result; see later).

One distinction worth making between two types of non-experimental research is the difference be- tween quasi-experimental research and case studies. The example I discussed earlier – in which we wanted to examine incidence of lung cancer among smokers and non-smokers, without trying to control who smokes and who doesn’t – is a quasi-experimental design. That is, it’s the same as an experiment, but we don’t control the predictors (IVs). We can still use statistics to analyse the results, it’s just that we have to be a lot more careful.

The alternative approach, case studies, aims to provide a very detailed description of one or a few instances. In general, you can’t use statistics to analyse the results of case studies, and it’s usually very hard to draw any general conclusions about “people in general” from a few isolated examples. However, case studies are very useful in some situations. Firstly, there are situations where you don’t have any alternative: neuropsychology has this issue a lot. Sometimes, you just can’t find a lot of people with brain damage in a specific area, so the only thing you can do is describe those cases that you do have in as much detail and with as much care as you can. However, there’s also some genuine advantages to case studies: because you don’t have as many people to study, you have the ability to invest lots of time and effort trying to understand the specific factors at play in each case. This is a very valuable thing to do. As a consequence, case studies can complement the more statistically-oriented approaches that you see in experimental and quasi-experimental designs. We won’t talk much about case studies in these lectures, but they are nevertheless very valuable tools!

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Determining the level of evidence: Nonexperimental research designs

Affiliation.

1 Amy Glasofer is a nurse scientist at Virtua Center for Learning in Mt. Laurel, N.J., and Ann B. Townsend is an adult NP with The Nurse Practitioner Group, LLC.
PMID: 33953103
DOI: 10.1097/01.NURSE.0000731852.39123.e1

To support evidence-based nursing practice, the authors provide guidelines for appraising research based on quality, quantity, and consistency. This article, the second of a three-part series, focuses on nonexperimental research designs.

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Overview of Non-Experimental Research

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

When to Use Non-Experimental Research

the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., how accurate are people’s first impressions?).
the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach is appropriate. But the two approaches can also be used to address the same research question in complementary ways. For example, in Milgram’s original (non-experimental) obedience study, he was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. However, Milgram subsequently conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into two broad categories: correlational research and observational research.

Cross-Sectional, Longitudinal, and Cross-Sequential Studies

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in psychiatric wards was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable. Figure 6.1 shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) falls in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the inability to randomly assign children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

A research that lacks the manipulation of an independent variable.

Research that is non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

Studies that involve comparing two or more pre-existing groups of people (e.g., children at different stages of development).

Differences between the groups may reflect the generation that people come from rather than a direct effect of age.

Studies in which one group of people are followed over time as they age.

Studies in which researchers follow people in different age groups in a smaller period of time.

Overview of Non-Experimental Research Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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v.11(2); 2019 Feb

Planning and Conducting Clinical Research: The Whole Process

Boon-how chew.

1 Family Medicine, Universiti Putra Malaysia, Serdang, MYS

The goal of this review was to present the essential steps in the entire process of clinical research. Research should begin with an educated idea arising from a clinical practice issue. A research topic rooted in a clinical problem provides the motivation for the completion of the research and relevancy for affecting medical practice changes and improvements. The research idea is further informed through a systematic literature review, clarified into a conceptual framework, and defined into an answerable research question. Engagement with clinical experts, experienced researchers, relevant stakeholders of the research topic, and even patients can enhance the research question’s relevance, feasibility, and efficiency. Clinical research can be completed in two major steps: study designing and study reporting. Three study designs should be planned in sequence and iterated until properly refined: theoretical design, data collection design, and statistical analysis design. The design of data collection could be further categorized into three facets: experimental or non-experimental, sampling or census, and time features of the variables to be studied. The ultimate aims of research reporting are to present findings succinctly and timely. Concise, explicit, and complete reporting are the guiding principles in clinical studies reporting.

Introduction and background

Medical and clinical research can be classified in many different ways. Probably, most people are familiar with basic (laboratory) research, clinical research, healthcare (services) research, health systems (policy) research, and educational research. Clinical research in this review refers to scientific research related to clinical practices. There are many ways a clinical research's findings can become invalid or less impactful including ignorance of previous similar studies, a paucity of similar studies, poor study design and implementation, low test agent efficacy, no predetermined statistical analysis, insufficient reporting, bias, and conflicts of interest [ 1 - 4 ]. Scientific, ethical, and moral decadence among researchers can be due to incognizant criteria in academic promotion and remuneration and too many forced studies by amateurs and students for the sake of research without adequate training or guidance [ 2 , 5 - 6 ]. This article will review the proper methods to conduct medical research from the planning stage to submission for publication (Table (Table1 1 ).

a Feasibility and efficiency are considered during the refinement of the research question and adhered to during data collection.

Concept	Research Idea	Research Question	Acquiring Data	Analysis	Publication	Practice
Actions	Relevant clinical problem or issue	Primary or secondary	Measuring	Prespecified	Writing skills	Guidelines
	Literature review	Quantitative or qualitative	Measuring tool	Predetermined	Guidelines	Protocol
	Conceptual framework	Causal or non-causal	Measurement	Exploratory allowed	Journal selection	Policy
	Collaboration with experts	Feasibility	Feasibility	Strength and direction of the effect estimate	Response to reviewers’ comments	Change
	Seek target population’s opinions on the research topic	Efficiency	Efficiency
		Theoretical Design	Data Collection Design	Statistical design
		Domain (external validity)	Experimental or non-experimental	Data cleaning
		Valid (confounding minimized)	Sampling or census	Outlier
		Precise (good sample size)	Time features	Missing data
		Pilot study		Descriptive
				Inferential
				Statistical assumptions
				Collaboration with statistician

Epidemiologic studies in clinical and medical fields focus on the effect of a determinant on an outcome [ 7 ]. Measurement errors that happen systematically give rise to biases leading to invalid study results, whereas random measurement errors will cause imprecise reporting of effects. Precision can usually be increased with an increased sample size provided biases are avoided or trivialized. Otherwise, the increased precision will aggravate the biases. Because epidemiologic, clinical research focuses on measurement, measurement errors are addressed throughout the research process. Obtaining the most accurate estimate of a treatment effect constitutes the whole business of epidemiologic research in clinical practice. This is greatly facilitated by clinical expertise and current scientific knowledge of the research topic. Current scientific knowledge is acquired through literature reviews or in collaboration with an expert clinician. Collaboration and consultation with an expert clinician should also include input from the target population to confirm the relevance of the research question. The novelty of a research topic is less important than the clinical applicability of the topic. Researchers need to acquire appropriate writing and reporting skills from the beginning of their careers, and these skills should improve with persistent use and regular reviewing of published journal articles. A published clinical research study stands on solid scientific ground to inform clinical practice given the article has passed through proper peer-reviews, revision, and content improvement.

Systematic literature reviews

Systematic literature reviews of published papers will inform authors of the existing clinical evidence on a research topic. This is an important step to reduce wasted efforts and evaluate the planned study [ 8 ]. Conducting a systematic literature review is a well-known important step before embarking on a new study [ 9 ]. A rigorously performed and cautiously interpreted systematic review that includes in-process trials can inform researchers of several factors [ 10 ]. Reviewing the literature will inform the choice of recruitment methods, outcome measures, questionnaires, intervention details, and statistical strategies – useful information to increase the study’s relevance, value, and power. A good review of previous studies will also provide evidence of the effects of an intervention that may or may not be worthwhile; this would suggest either no further studies are warranted or that further study of the intervention is needed. A review can also inform whether a larger and better study is preferable to an additional small study. Reviews of previously published work may yield few studies or low-quality evidence from small or poorly designed studies on certain intervention or observation; this may encourage or discourage further research or prompt consideration of a first clinical trial.

Conceptual framework

The result of a literature review should include identifying a working conceptual framework to clarify the nature of the research problem, questions, and designs, and even guide the latter discussion of the findings and development of possible solutions. Conceptual frameworks represent ways of thinking about a problem or how complex things work the way they do [ 11 ]. Different frameworks will emphasize different variables and outcomes, and their inter-relatedness. Each framework highlights or emphasizes different aspects of a problem or research question. Often, any single conceptual framework presents only a partial view of reality [ 11 ]. Furthermore, each framework magnifies certain elements of the problem. Therefore, a thorough literature search is warranted for authors to avoid repeating the same research endeavors or mistakes. It may also help them find relevant conceptual frameworks including those that are outside one’s specialty or system.

Conceptual frameworks can come from theories with well-organized principles and propositions that have been confirmed by observations or experiments. Conceptual frameworks can also come from models derived from theories, observations or sets of concepts or even evidence-based best practices derived from past studies [ 11 ].

Researchers convey their assumptions of the associations of the variables explicitly in the conceptual framework to connect the research to the literature. After selecting a single conceptual framework or a combination of a few frameworks, a clinical study can be completed in two fundamental steps: study design and study report. Three study designs should be planned in sequence and iterated until satisfaction: the theoretical design, data collection design, and statistical analysis design [ 7 ].

Study designs

Theoretical Design

Theoretical design is the next important step in the research process after a literature review and conceptual framework identification. While the theoretical design is a crucial step in research planning, it is often dealt with lightly because of the more alluring second step (data collection design). In the theoretical design phase, a research question is designed to address a clinical problem, which involves an informed understanding based on the literature review and effective collaboration with the right experts and clinicians. A well-developed research question will have an initial hypothesis of the possible relationship between the explanatory variable/exposure and the outcome. This will inform the nature of the study design, be it qualitative or quantitative, primary or secondary, and non-causal or causal (Figure (Figure1 1 ).

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A study is qualitative if the research question aims to explore, understand, describe, discover or generate reasons underlying certain phenomena. Qualitative studies usually focus on a process to determine how and why things happen [ 12 ]. Quantitative studies use deductive reasoning, and numerical statistical quantification of the association between groups on data often gathered during experiments [ 13 ]. A primary clinical study is an original study gathering a new set of patient-level data. Secondary research draws on the existing available data and pooling them into a larger database to generate a wider perspective or a more powerful conclusion. Non-causal or descriptive research aims to identify the determinants or associated factors for the outcome or health condition, without regard for causal relationships. Causal research is an exploration of the determinants of an outcome while mitigating confounding variables. Table Table2 2 shows examples of non-causal (e.g., diagnostic and prognostic) and causal (e.g., intervention and etiologic) clinical studies. Concordance between the research question, its aim, and the choice of theoretical design will provide a strong foundation and the right direction for the research process and path.

Research Category	Study Title
Diagnostic	Plasma Concentration of B-type Natriuretic Peptide (BNP) in the Diagnosis of Left Ventricular Dysfunction
Diagnostic	The Centor and McIsaac Scores and the Group A Streptococcal Pharyngitis
Prognostic	The Apgar Score and Infant Mortality
Prognostic	SCORE (Systematic COronary Risk Evaluation) for the Estimation of Ten-Year Risk of Fatal Cardiovascular Disease
Intervention	Dexamethasone in Very Low Birth Weight Infants
Intervention	Bariatric Surgery of Obesity in Type 2 Diabetes and Metabolic Syndrome
Etiologic	Thalidomide and Reduction Deformities of the Limbs
Etiologic	Work Stress and Risk of Cardiovascular Mortality

A problem in clinical epidemiology is phrased in a mathematical relationship below, where the outcome is a function of the determinant (D) conditional on the extraneous determinants (ED) or more commonly known as the confounding factors [ 7 ]:

For non-causal research, Outcome = f (D1, D2…Dn) For causal research, Outcome = f (D | ED)

A fine research question is composed of at least three components: 1) an outcome or a health condition, 2) determinant/s or associated factors to the outcome, and 3) the domain. The outcome and the determinants have to be clearly conceptualized and operationalized as measurable variables (Table (Table3; 3 ; PICOT [ 14 ] and FINER [ 15 ]). The study domain is the theoretical source population from which the study population will be sampled, similar to the wording on a drug package insert that reads, “use this medication (study results) in people with this disease” [ 7 ].

Acronym	Explanation
P =	Patient (or the domain)
I =	Intervention or treatment (or the determinants in non-experimental)
C =	Comparison (only in experimental)
O =	Outcome
T =	Time describes the duration of data collection
F =	Feasible with the current and/or potential available resources
I =	Important and interesting to current clinical practice and to you, respectively
N =	Novel and adding to the existing corpus of scientific knowledge
E =	Ethical research conducted without harm to participants and institutions
R =	Relevant to as many parties as possible, not only to your own practice

The interpretation of study results as they apply to wider populations is known as generalization, and generalization can either be statistical or made using scientific inferences [ 16 ]. Generalization supported by statistical inferences is seen in studies on disease prevalence where the sample population is representative of the source population. By contrast, generalizations made using scientific inferences are not bound by the representativeness of the sample in the study; rather, the generalization should be plausible from the underlying scientific mechanisms as long as the study design is valid and nonbiased. Scientific inferences and generalizations are usually the aims of causal studies.

Confounding: Confounding is a situation where true effects are obscured or confused [ 7 , 16 ]. Confounding variables or confounders affect the validity of a study’s outcomes and should be prevented or mitigated in the planning stages and further managed in the analytical stages. Confounders are also known as extraneous determinants in epidemiology due to their inherent and simultaneous relationships to both the determinant and outcome (Figure (Figure2), 2 ), which are usually one-determinant-to-one outcome in causal clinical studies. The known confounders are also called observed confounders. These can be minimized using randomization, restriction, or a matching strategy. Residual confounding has occurred in a causal relationship when identified confounders were not measured accurately. Unobserved confounding occurs when the confounding effect is present as a variable or factor not observed or yet defined and, thus, not measured in the study. Age and gender are almost universal confounders followed by ethnicity and socio-economic status.

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Object name is cureus-0011-00000004112-i02.jpg

Confounders have three main characteristics. They are a potential risk factor for the disease, associated with the determinant of interest, and should not be an intermediate variable between the determinant and the outcome or a precursor to the determinant. For example, a sedentary lifestyle is a cause for acute coronary syndrome (ACS), and smoking could be a confounder but not cardiorespiratory unfitness (which is an intermediate factor between a sedentary lifestyle and ACS). For patients with ACS, not having a pair of sports shoes is not a confounder – it is a correlate for the sedentary lifestyle. Similarly, depression would be a precursor, not a confounder.

Sample size consideration: Sample size calculation provides the required number of participants to be recruited in a new study to detect true differences in the target population if they exist. Sample size calculation is based on three facets: an estimated difference in group sizes, the probability of α (Type I) and β (Type II) errors chosen based on the nature of the treatment or intervention, and the estimated variability (interval data) or proportion of the outcome (nominal data) [ 17 - 18 ]. The clinically important effect sizes are determined based on expert consensus or patients’ perception of benefit. Value and economic consideration have increasingly been included in sample size estimations. Sample size and the degree to which the sample represents the target population affect the accuracy and generalization of a study’s reported effects.

Pilot study: Pilot studies assess the feasibility of the proposed research procedures on small sample size. Pilot studies test the efficiency of participant recruitment with minimal practice or service interruptions. Pilot studies should not be conducted to obtain a projected effect size for a larger study population because, in a typical pilot study, the sample size is small, leading to a large standard error of that effect size. This leads to bias when projected for a large population. In the case of underestimation, this could lead to inappropriately terminating the full-scale study. As the small pilot study is equally prone to bias of overestimation of the effect size, this would lead to an underpowered study and a failed full-scale study [ 19 ].

The Design of Data Collection

The “perfect” study design in the theoretical phase now faces the practical and realistic challenges of feasibility. This is the step where different methods for data collection are considered, with one selected as the most appropriate based on the theoretical design along with feasibility and efficiency. The goal of this stage is to achieve the highest possible validity with the lowest risk of biases given available resources and existing constraints.

In causal research, data on the outcome and determinants are collected with utmost accuracy via a strict protocol to maximize validity and precision. The validity of an instrument is defined as the degree of fidelity of the instrument, measuring what it is intended to measure, that is, the results of the measurement correlate with the true state of an occurrence. Another widely used word for validity is accuracy. Internal validity refers to the degree of accuracy of a study’s results to its own study sample. Internal validity is influenced by the study designs, whereas the external validity refers to the applicability of a study’s result in other populations. External validity is also known as generalizability and expresses the validity of assuming the similarity and comparability between the study population and the other populations. Reliability of an instrument denotes the extent of agreeableness of the results of repeated measurements of an occurrence by that instrument at a different time, by different investigators or in a different setting. Other terms that are used for reliability include reproducibility and precision. Preventing confounders by identifying and including them in data collection will allow statistical adjustment in the later analyses. In descriptive research, outcomes must be confirmed with a referent standard, and the determinants should be as valid as those found in real clinical practice.

Common designs for data collection include cross-sectional, case-control, cohort, and randomized controlled trials (RCTs). Many other modern epidemiology study designs are based on these classical study designs such as nested case-control, case-crossover, case-control without control, and stepwise wedge clustered RCTs. A cross-sectional study is typically a snapshot of the study population, and an RCT is almost always a prospective study. Case-control and cohort studies can be retrospective or prospective in data collection. The nested case-control design differs from the traditional case-control design in that it is “nested” in a well-defined cohort from which information on the cohorts can be obtained. This design also satisfies the assumption that cases and controls represent random samples of the same study base. Table Table4 4 provides examples of these data collection designs.

Data Collection Designs	Study Title
Cross-sectional	The National Health and Morbidity Survey (NHMS)
	The National Health and Nutrition Examination Survey (NHANES)
Cohort	Framingham Heart Study
	The Malaysian Cohort (TMC) project
Case-control	A Case-Control Study of the Effectiveness of Bicycle Safety Helmets
	Open-Angle Glaucoma and Ocular Hypertension: the Long Island Glaucoma Case-Control Study
Nested case-control	Nurses' Health Study on Plasma Adipokines and Endometriosis Risk
	Physicians' Health Study Plasma Homocysteine and Risk of Myocardial Infarction
Randomized controlled trial	The Women’s Health Initiative
	U.K. Prospective Diabetes Study
Cross-over	Intranasal-agonist in Allergic Rhinitis Published in the Allergy in 2000
	Effect of Palm-based Tocotrienols and Tocopherol Mixture Supplementation on Platelet Aggregation in Subjects with Metabolic Syndrome

Additional aspects in data collection: No single design of data collection for any research question as stated in the theoretical design will be perfect in actual conduct. This is because of myriad issues facing the investigators such as the dynamic clinical practices, constraints of time and budget, the urgency for an answer to the research question, and the ethical integrity of the proposed experiment. Therefore, feasibility and efficiency without sacrificing validity and precision are important considerations in data collection design. Therefore, data collection design requires additional consideration in the following three aspects: experimental/non-experimental, sampling, and timing [ 7 ]:

Experimental or non-experimental: Non-experimental research (i.e., “observational”), in contrast to experimental, involves data collection of the study participants in their natural or real-world environments. Non-experimental researches are usually the diagnostic and prognostic studies with cross-sectional in data collection. The pinnacle of non-experimental research is the comparative effectiveness study, which is grouped with other non-experimental study designs such as cross-sectional, case-control, and cohort studies [ 20 ]. It is also known as the benchmarking-controlled trials because of the element of peer comparison (using comparable groups) in interpreting the outcome effects [ 20 ]. Experimental study designs are characterized by an intervention on a selected group of the study population in a controlled environment, and often in the presence of a similar group of the study population to act as a comparison group who receive no intervention (i.e., the control group). Thus, the widely known RCT is classified as an experimental design in data collection. An experimental study design without randomization is referred to as a quasi-experimental study. Experimental studies try to determine the efficacy of a new intervention on a specified population. Table Table5 5 presents the advantages and disadvantages of experimental and non-experimental studies [ 21 ].

a May be an issue in cross-sectional studies that require a long recall to the past such as dietary patterns, antenatal events, and life experiences during childhood.

Non-experimental	Experimental
Advantages
Quick results are possible	Comparable groups
Relatively less costly	Hawthorne and placebo effects mitigated
No recall bias	Straightforward, robust statistical analysis
No time effects	Convincing results as evidence
Real-life data
Disadvantages
Observed, unobserved, and residual confounding	Expensive
	Time-consuming
	Overly controlled environment
	Loss to follow-up
	Random allocation of potentially harmful treatment may not be ethically permissible

Once an intervention yields a proven effect in an experimental study, non-experimental and quasi-experimental studies can be used to determine the intervention’s effect in a wider population and within real-world settings and clinical practices. Pragmatic or comparative effectiveness are the usual designs used for data collection in these situations [ 22 ].

Sampling/census: Census is a data collection on the whole source population (i.e., the study population is the source population). This is possible when the defined population is restricted to a given geographical area. A cohort study uses the census method in data collection. An ecologic study is a cohort study that collects summary measures of the study population instead of individual patient data. However, many studies sample from the source population and infer the results of the study to the source population for feasibility and efficiency because adequate sampling provides similar results to the census of the whole population. Important aspects of sampling in research planning are sample size and representation of the population. Sample size calculation accounts for the number of participants needed to be in the study to discover the actual association between the determinant and outcome. Sample size calculation relies on the primary objective or outcome of interest and is informed by the estimated possible differences or effect size from previous similar studies. Therefore, the sample size is a scientific estimation for the design of the planned study.

A sampling of participants or cases in a study can represent the study population and the larger population of patients in that disease space, but only in prevalence, diagnostic, and prognostic studies. Etiologic and interventional studies do not share this same level of representation. A cross-sectional study design is common for determining disease prevalence in the population. Cross-sectional studies can also determine the referent ranges of variables in the population and measure change over time (e.g., repeated cross-sectional studies). Besides being cost- and time-efficient, cross-sectional studies have no loss to follow-up; recall bias; learning effect on the participant; or variability over time in equipment, measurement, and technician. A cross-sectional design for an etiologic study is possible when the determinants do not change with time (e.g., gender, ethnicity, genetic traits, and blood groups).

In etiologic research, comparability between the exposed and the non-exposed groups is more important than sample representation. Comparability between these two groups will provide an accurate estimate of the effect of the exposure (risk factor) on the outcome (disease) and enable valid inference of the causal relation to the domain (the theoretical population). In a case-control study, a sampling of the control group should be taken from the same study population (study base), have similar profiles to the cases (matching) but do not have the outcome seen in the cases. Matching important factors minimizes the confounding of the factors and increases statistical efficiency by ensuring similar numbers of cases and controls in confounders’ strata [ 23 - 24 ]. Nonetheless, perfect matching is neither necessary nor achievable in a case-control study because a partial match could achieve most of the benefits of the perfect match regarding a more precise estimate of odds ratio than statistical control of confounding in unmatched designs [ 25 - 26 ]. Moreover, perfect or full matching can lead to an underestimation of the point estimates [ 27 - 28 ].

Time feature: The timing of data collection for the determinant and outcome characterizes the types of studies. A cross-sectional study has the axis of time zero (T = 0) for both the determinant and the outcome, which separates it from all other types of research that have time for the outcome T > 0. Retrospective or prospective studies refer to the direction of data collection. In retrospective studies, information on the determinant and outcome have been collected or recorded before. In prospective studies, this information will be collected in the future. These terms should not be used to describe the relationship between the determinant and the outcome in etiologic studies. Time of exposure to the determinant, the time of induction, and the time at risk for the outcome are important aspects to understand. Time at risk is the period of time exposed to the determinant risk factors. Time of induction is the time from the sufficient exposure to the risk or causal factors to the occurrence of a disease. The latent period is when the occurrence of a disease without manifestation of the disease such as in “silence” diseases for example cancers, hypertension and type 2 diabetes mellitus which is detected from screening practices. Figure Figure3 3 illustrates the time features of a variable. Variable timing is important for accurate data capture.

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The Design of Statistical Analysis

Statistical analysis of epidemiologic data provides the estimate of effects after correcting for biases (e.g., confounding factors) measures the variability in the data from random errors or chance [ 7 , 16 , 29 ]. An effect estimate gives the size of an association between the studied variables or the level of effectiveness of an intervention. This quantitative result allows for comparison and assessment of the usefulness and significance of the association or the intervention between studies. This significance must be interpreted with a statistical model and an appropriate study design. Random errors could arise in the study resulting from unexplained personal choices by the participants. Random error is, therefore, when values or units of measurement between variables change in non-concerted or non-directional manner. Conversely, when these values or units of measurement between variables change in a concerted or directional manner, we note a significant relationship as shown by statistical significance.

Variability: Researchers almost always collect the needed data through a sampling of subjects/participants from a population instead of a census. The process of sampling or multiple sampling in different geographical regions or over different periods contributes to varied information due to the random inclusion of different participants and chance occurrence. This sampling variation becomes the focus of statistics when communicating the degree and intensity of variation in the sampled data and the level of inference in the population. Sampling variation can be influenced profoundly by the total number of participants and the width of differences of the measured variable (standard deviation). Hence, the characteristics of the participants, measurements and sample size are all important factors in planning a study.

Statistical strategy: Statistical strategy is usually determined based on the theoretical and data collection designs. Use of a prespecified statistical strategy (including the decision to dichotomize any continuous data at certain cut-points, sub-group analysis or sensitive analyses) is recommended in the study proposal (i.e., protocol) to prevent data dredging and data-driven reports that predispose to bias. The nature of the study hypothesis also dictates whether directional (one-tailed) or non-directional (two-tailed) significance tests are conducted. In most studies, two-sided tests are used except in specific instances when unidirectional hypotheses may be appropriate (e.g., in superiority or non-inferiority trials). While data exploration is discouraged, epidemiological research is, by nature of its objectives, statistical research. Hence, it is acceptable to report the presence of persistent associations between any variables with plausible underlying mechanisms during the exploration of the data. The statistical methods used to produce the results should be explicitly explained. Many different statistical tests are used to handle various kinds of data appropriately (e.g., interval vs discrete), and/or the various distribution of the data (e.g., normally distributed or skewed). For additional details on statistical explanations and underlying concepts of statistical tests, readers are recommended the references as cited in this sentence [ 30 - 31 ].

Steps in statistical analyses: Statistical analysis begins with checking for data entry errors. Duplicates are eliminated, and proper units should be confirmed. Extremely low, high or suspicious values are confirmed from the source data again. If this is not possible, this is better classified as a missing value. However, if the unverified suspicious data are not obviously wrong, they should be further examined as an outlier in the analysis. The data checking and cleaning enables the analyst to establish a connection with the raw data and to anticipate possible results from further analyses. This initial step involves descriptive statistics that analyze central tendency (i.e., mode, median, and mean) and dispersion (i.e., (minimum, maximum, range, quartiles, absolute deviation, variance, and standard deviation) of the data. Certain graphical plotting such as scatter plot, a box-whiskers plot, histogram or normal Q-Q plot are helpful at this stage to verify data normality in distribution. See Figure Figure4 4 for the statistical tests available for analyses of different types of data.

An external file that holds a picture, illustration, etc.
Object name is cureus-0011-00000004112-i04.jpg

Once data characteristics are ascertained, further statistical tests are selected. The analytical strategy sometimes involves the transformation of the data distribution for the selected tests (e.g., log, natural log, exponential, quadratic) or for checking the robustness of the association between the determinants and their outcomes. This step is also referred to as inferential statistics whereby the results are about hypothesis testing and generalization to the wider population that the study’s sampled participants represent. The last statistical step is checking whether the statistical analyses fulfill the assumptions of that particular statistical test and model to avoid violation and misleading results. These assumptions include evaluating normality, variance homogeneity, and residuals included in the final statistical model. Other statistical values such as Akaike information criterion, variance inflation factor/tolerance, and R2 are also considered when choosing the best-fitted models. Transforming raw data could be done, or a higher level of statistical analyses can be used (e.g., generalized linear models and mixed-effect modeling). Successful statistical analysis allows conclusions of the study to fit the data.

Bayesian and Frequentist statistical frameworks: Most of the current clinical research reporting is based on the frequentist approach and hypotheses testing p values and confidence intervals. The frequentist approach assumes the acquired data are random, attained by random sampling, through randomized experiments or influences, and with random errors. The distribution of the data (its point estimate and confident interval) infers a true parameter in the real population. The major conceptual difference between Bayesian statistics and frequentist statistics is that in Bayesian statistics, the parameter (i.e., the studied variable in the population) is random and the data acquired is real (true or fix). Therefore, the Bayesian approach provides a probability interval for the parameter. The studied parameter is random because it could vary and be affected by prior beliefs, experience or evidence of plausibility. In the Bayesian statistical approach, this prior belief or available knowledge is quantified into a probability distribution and incorporated into the acquired data to get the results (i.e., the posterior distribution). This uses mathematical theory of Bayes’ Theorem to “turn around” conditional probabilities.

The goal of research reporting is to present findings succinctly and timely via conference proceedings or journal publication. Concise and explicit language use, with all the necessary details to enable replication and judgment of the study applicability, are the guiding principles in clinical studies reporting.

Writing for Reporting

Medical writing is very much a technical chore that accommodates little artistic expression. Research reporting in medicine and health sciences emphasize clear and standardized reporting, eschewing adjectives and adverbs extensively used in popular literature. Regularly reviewing published journal articles can familiarize authors with proper reporting styles and help enhance writing skills. Authors should familiarize themselves with standard, concise, and appropriate rhetoric for the intended audience, which includes consideration for journal reviewers, editors, and referees. However, proper language can be somewhat subjective. While each publication may have varying requirements for submission, the technical requirements for formatting an article are usually available via author or submission guidelines provided by the target journal.

Research reports for publication often contain a title, abstract, introduction, methods, results, discussion, and conclusions section, and authors may want to write each section in sequence. However, best practices indicate the abstract and title should be written last. Authors may find that when writing one section of the report, ideas come to mind that pertains to other sections, so careful note taking is encouraged. One effective approach is to organize and write the result section first, followed by the discussion and conclusions sections. Once these are drafted, write the introduction, abstract, and the title of the report. Regardless of the sequence of writing, the author should begin with a clear and relevant research question to guide the statistical analyses, result interpretation, and discussion. The study findings can be a motivator to propel the author through the writing process, and the conclusions can help the author draft a focused introduction.

Writing for Publication

Specific recommendations on effective medical writing and table generation are available [ 32 ]. One such resource is Effective Medical Writing: The Write Way to Get Published, which is an updated collection of medical writing articles previously published in the Singapore Medical Journal [ 33 ]. The British Medical Journal’s Statistics Notes series also elucidates common and important statistical concepts and usages in clinical studies. Writing guides are also available from individual professional societies, journals, or publishers such as Chest (American College of Physicians) medical writing tips, PLoS Reporting guidelines collection, Springer’s Journal Author Academy, and SAGE’s Research methods [ 34 - 37 ]. Standardized research reporting guidelines often come in the form of checklists and flow diagrams. Table Table6 6 presents a list of reporting guidelines. A full compilation of these guidelines is available at the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network website [ 38 ] which aims to improve the reliability and value of medical literature by promoting transparent and accurate reporting of research studies. Publication of the trial protocol in a publicly available database is almost compulsory for publication of the full report in many potential journals.

No.	Reporting Guidelines and Checklists
	CONSORT - CONsolidated Standards Of Reporting Trials
	A 25-item checklist for reporting of randomized controlled trials. There are appropriate extensions to the CONSORT statement due to variations in the standard trial methodology such as different design aspects (e.g., cluster, pragmatic, non-inferiority and equivalence trials), interventions (e.g., herbals) and data (e.g., harms, including the extension for writing abstracts)
	SPIRIT - Standard Protocol Items: Recommendations for Interventional Trials
	A 33-item checklist for reporting protocols for randomized controlled trials
	COREQ - COnsolidated criteria for REporting Qualitative research
	A 32-item checklist for reporting qualitative research of interviews and focus groups
	STARD - STAndards for the Reporting of Diagnostic accuracy studies
	A 25-item checklist for reporting of diagnostic accuracy studies
	PRISMA - Preferred Reporting Items for Systematic reviews and Meta-Analyses
	A 27-item checklist for reporting of systematic reviews
	PRISMA-P - Preferred Reporting Items for Systematic reviews and Meta-Analyses Protocols
	A 17-item checklist for reporting of systematic review and meta-analysis protocols
	MOOSE - Meta-analysis Of Observational Studies in Epidemiology
	A 35-item checklist for reporting of meta-analyses of observational studies
	STROBE - STrengthening the Reporting of OBservational studies in Epidemiology
	For reporting of observational studies in epidemiology
		Checklist for cohort, case-control and cross-sectional studies (combined)
		Checklist for cohort studies
		Checklist for case-control studies
		Checklist for cross-sectional studies
	Extensions of the STROBE statement
	STROME-ID - STrengthening the Reporting Of Molecular Epidemiology for Infectious Diseases
	A 42-item checklist
	STREGA - STrengthening the REporting of Genetic Associations
	A 22-item checklist for reporting of gene-disease association studies
	CHEERS - Consolidated Health Economic Evaluation Reporting Standards
	A 24-item checklist for reporting of health economic evaluations

Graphics and Tables

Graphics and tables should emphasize salient features of the underlying data and should coherently summarize large quantities of information. Although graphics provide a break from dense prose, authors must not forget that these illustrations should be scientifically informative, not decorative. The titles for graphics and tables should be clear, informative, provide the sample size, and use minimal font weight and formatting only to distinguish headings, data entry or to highlight certain results. Provide a consistent number of decimal points for the numerical results, and with no more than four for the P value. Most journals prefer cell-delineated tables created using the table function in word processing or spreadsheet programs. Some journals require specific table formatting such as the absence or presence of intermediate horizontal lines between cells.

Decisions of authorship are both sensitive and important and should be made at an early stage by the study’s stakeholders. Guidelines and journals’ instructions to authors abound with authorship qualifications. The guideline on authorship by the International Committee of Medical Journal Editors is widely known and provides a standard used by many medical and clinical journals [ 39 ]. Generally, authors are those who have made major contributions to the design, conduct, and analysis of the study, and who provided critical readings of the manuscript (if not involved directly in manuscript writing).

Picking a target journal for submission

Once a report has been written and revised, the authors should select a relevant target journal for submission. Authors should avoid predatory journals—publications that do not aim to advance science and disseminate quality research. These journals focus on commercial gain in medical and clinical publishing. Two good resources for authors during journal selection are Think-Check-Submit and the defunct Beall's List of Predatory Publishers and Journals (now archived and maintained by an anonymous third-party) [ 40 , 41 ]. Alternatively, reputable journal indexes such as Thomson Reuters Journal Citation Reports, SCOPUS, MedLine, PubMed, EMBASE, EBSCO Publishing's Electronic Databases are available areas to start the search for an appropriate target journal. Authors should review the journals’ names, aims/scope, and recently published articles to determine the kind of research each journal accepts for publication. Open-access journals almost always charge article publication fees, while subscription-based journals tend to publish without author fees and instead rely on subscription or access fees for the full text of published articles.

Conclusions

Conducting a valid clinical research requires consideration of theoretical study design, data collection design, and statistical analysis design. Proper study design implementation and quality control during data collection ensures high-quality data analysis and can mitigate bias and confounders during statistical analysis and data interpretation. Clear, effective study reporting facilitates dissemination, appreciation, and adoption, and allows the researchers to affect real-world change in clinical practices and care models. Neutral or absence of findings in a clinical study are as important as positive or negative findings. Valid studies, even when they report an absence of expected results, still inform scientific communities of the nature of a certain treatment or intervention, and this contributes to future research, systematic reviews, and meta-analyses. Reporting a study adequately and comprehensively is important for accuracy, transparency, and reproducibility of the scientific work as well as informing readers.

Acknowledgments

The author would like to thank Universiti Putra Malaysia and the Ministry of Higher Education, Malaysia for their support in sponsoring the Ph.D. study and living allowances for Boon-How Chew.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The materials presented in this paper is being organized by the author into a book.

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Difference Between Experimental and Non-Experimental Research
What is non-experimental research: Definition, types & examples
Research Design:Non Experimental (Quantitative) Easy and simple explanation
Non-Experimental Research Methods in Quantitative Research
Quantitative Research Designs: Non-Experimental vs. Experimental
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Quantitative Research || Research || Meaning and Characteristics
QUANTITATIVE RESEARCH
non experimental research design with examples and characteristics
Quantitative Non- Experimental Designs
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COMMENTS

Quantitative Research with Nonexperimental Designs
Leung and Shek (2018) explain: Experimental research design utilizes the principle of manipulation of the independent variables and examines its cause-and-effect relationship on the dependent variables by controlling the effects of other variables. Usually, the experimenter assigns two or more groups with similar characteristics.
6.1 Overview of Non-Experimental Research
Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world). Most researchers in psychology consider the distinction between experimental ...
Quantitative Research Designs: Non-Experimental vs. Experimental
Without this level of control, you cannot determine any causal effects. While validity is still a concern in non-experimental research, the concerns are more about the validity of the measurements, rather than the validity of the effects. Finally, a quasi-experimental design is a combination of the two designs described above.
Non-experimental research: What it is, Types & Tips
Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information. Unlike experimental research, where the variables are held constant, non-experimental research happens during the study when the researcher ...
7.1 Overview of Nonexperimental Research
Key Takeaways. Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both. There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables.
Overview of Nonexperimental Research
Key Takeaways. Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both. There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables.
What is non-experimental research: Definition, types & examples
Non-experimental research is a type of research design that is based on observation and measuring instead of experimentation with randomly assigned participants. What characterizes this research design is the fact that it lacks the manipulation of independent variables. Because of this fact, the non-experimental research is based on naturally ...
Overview of Non-Experimental Research
Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world). Most researchers in psychology consider the distinction between experimental ...
1.6: Non-Experimental Research
Non-experimental research is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world). Most researchers in social sciences consider the distinction between ...
Non-Experimental Research: Overview & Advantages
Non-experimental designs can open a number of advantageous research opportunities. The benefits include: Non-experimental research can be used to analyze events that have happened in the past. The versatility of the model can be used to observe many unique phenomena. This method of research is far more affordable than the experimental kind.
A Primer to Experimental and Nonexperimental Quantitative Research: The
If you have ever been curious about terms like clinical trial, correlation study, or survey, then this article is for you.It will hopefully help you gain a better understanding of what these and many more related terms mean as they all fall under the wider umbrella term of quantitative research.Here, we will focus on the fundamentals of quantitative research and explore major areas of inquiry ...
Experimental Vs Non-Experimental Research: 15 Key Differences
Experimental research is mostly used to make scientific innovations and find major solutions to problems while non-experimental research is used to define subject characteristics, measure data trends, compare situations and validate existing conditions. ... An example of a non-experimental quantitative research method is correlational research.
Nonexperimental research: strengths, weaknesses and ...
Nonexperimental research, defined as any kind of quantitative or qualitative research that is not an experiment, is the predominate kind of research design used in the social sciences. How to unambiguously and correctly present the results of nonexperimental research, however, remains decidedly unclear and possibly detrimental to applied ...
What Is Quantitative Research?
Quantitative research methods. You can use quantitative research methods for descriptive, correlational or experimental research. In descriptive research, you simply seek an overall summary of your study variables.; In correlational research, you investigate relationships between your study variables.; In experimental research, you systematically examine whether there is a cause-and-effect ...
2.5: Experimental and Non-experimental Research
2.5: Experimental and Non-experimental Research. One of the big distinctions that you should be aware of is the distinction between "experimental research" and "non-experimental research". When we make this distinction, what we're really talking about is the degree of control that the researcher exercises over the people and events in ...
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Humans. Research Design*. To support evidence-based nursing practice, the authors provide guidelines for appraising research based on quality, quantity, and consistency. This article, the second of a three-part series, focuses on nonexperimental research designs.
Overview of Non-Experimental Research
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Planning and Conducting Clinical Research: The Whole Process
Non-experimental researches are usually the diagnostic and prognostic studies with cross-sectional in data collection. The pinnacle of non-experimental research is the comparative effectiveness study, which is grouped with other non-experimental study designs such as cross-sectional, case-control, and cohort studies .
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What is Experimental Research?

Pre-experimental Research

Quasi-experimental Research