descriptive research and variables

• Privacy Policy

Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Case Study – Methods, Examples and Guide

Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

FREE TRIAL         LEARN MORE

MORE LIKE THIS

What Are My Employees Really Thinking? The Power of Open-ended Survey Analysis

May 24, 2024

I Am Disconnected – Tuesday CX Thoughts

May 21, 2024

20 Best Customer Success Tools of 2024

May 20, 2024

AI-Based Services Buying Guide for Market Research (based on ESOMAR’s 20 Questions) 

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

Prevent plagiarism, run a free check.

Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Descriptive Research Design | Definition, Methods & Examples. Scribbr. Retrieved 26 May 2024, from https://www.scribbr.co.uk/research-methods/descriptive-research-design/

Is this article helpful?

Shona McCombes

Other students also liked, a quick guide to experimental design | 5 steps & examples, correlational research | guide, design & examples, qualitative vs quantitative research | examples & methods.

What is Descriptive Research and How is it Used?

Introduction

What does descriptive research mean, why would you use a descriptive research design, what are the characteristics of descriptive research, examples of descriptive research, what are the data collection methods in descriptive research, how do you analyze descriptive research data, ensuring validity and reliability in the findings.

Conducting descriptive research offers researchers a way to present phenomena as they naturally occur. Rooted in an open-ended and non-experimental nature, this type of research focuses on portraying the details of specific phenomena or contexts, helping readers gain a clearer understanding of topics of interest.

From businesses gauging customer satisfaction to educators assessing classroom dynamics, the data collected from descriptive research provides invaluable insights across various fields.

This article aims to illuminate the essence, utility, characteristics, and methods associated with descriptive research, guiding those who wish to harness its potential in their respective domains.

At its core, descriptive research refers to a systematic approach used by researchers to collect, analyze, and present data about real-life phenomena to describe it in its natural context. It primarily aims to describe what exists, based on empirical observations .

Unlike experimental research, where variables are manipulated to observe outcomes, descriptive research deals with the "as-is" scenario to facilitate further research by providing a framework or new insights on which continuing studies can build.

Definition of descriptive research

Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon.

The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.

The difference between descriptive and exploratory research

While both descriptive and exploratory research seek to provide insights into a topic or phenomenon, they differ in their focus. Exploratory research is more about investigating a topic to develop preliminary insights or to identify potential areas of interest.

In contrast, descriptive research offers detailed accounts and descriptions of the observed phenomenon, seeking to paint a full picture of what's happening.

The evolution of descriptive research in academia

Historically, descriptive research has played a foundational role in numerous academic disciplines. Anthropologists, for instance, used this approach to document cultures and societies. Psychologists have employed it to capture behaviors, emotions, and reactions.

Over time, the method has evolved, incorporating technological advancements and adapting to contemporary needs, yet its essence remains rooted in describing a phenomenon or setting as it is.

Descriptive research serves as a cornerstone in the research landscape for its ability to provide a detailed snapshot of life. Its unique qualities and methods make it an invaluable method for various research purposes. Here's why:

Benefits of obtaining a clear picture

Descriptive research captures the present state of phenomena, offering researchers a detailed reflection of situations. This unaltered representation is crucial for sectors like marketing, where understanding current consumer behavior can shape future strategies.

Facilitating data interpretation

Given its straightforward nature, descriptive research can provide data that's easier to interpret, both for researchers and their audiences. Rather than analyzing complex statistical relationships among variables, researchers present detailed descriptions of their qualitative observations . Researchers can engage in in depth analysis relating to their research question , but audiences can also draw insights from their own interpretations or reflections on potential underlying patterns.

Enhancing the clarity of the research problem

By presenting things as they are, descriptive research can help elucidate ambiguous research questions. A well-executed descriptive study can shine light on overlooked aspects of a problem, paving the way for further investigative research.

Addressing practical problems

In real-world scenarios, it's not always feasible to manipulate variables or set up controlled experiments. For instance, in social sciences, understanding cultural norms without interference is paramount. Descriptive research allows for such non-intrusive insights, ensuring genuine understanding.

Building a foundation for future research

Often, descriptive studies act as stepping stones for more complex research endeavors. By establishing baseline data and highlighting patterns, they create a platform upon which more intricate hypotheses can be built and tested in subsequent studies.

Descriptive research is distinguished by a set of hallmark characteristics that set it apart from other research methodologies . Recognizing these features can help researchers effectively design, implement , and interpret descriptive studies.

Specificity in the research question

As with all research, descriptive research starts with a well-defined research question aiming to detail a particular phenomenon. The specificity ensures that the study remains focused on gathering relevant data without unnecessary deviations.

Focus on the present situation

While some research methods aim to predict future trends or uncover historical truths, descriptive research is predominantly concerned with the present. It seeks to capture the current state of affairs, such as understanding today's consumer habits or documenting a newly observed phenomenon.

Standardized and structured methodology

To ensure credibility and consistency in results, descriptive research often employs standardized methods. Whether it's using a fixed set of survey questions or adhering to specific observation protocols, this structured approach ensures that data is collected uniformly, making it easier to compare and analyze.

Non-manipulative approach in observation

One of the standout features of descriptive research is its non-invasive nature. Researchers observe and document without influencing the research subject or the environment. This passive stance ensures that the data gathered is a genuine reflection of the phenomenon under study.

Replicability and consistency in results

Due to its structured methodology, findings from descriptive research can often be replicated in different settings or with different samples. This consistency adds to the credibility of the results, reinforcing the validity of the insights drawn from the study.

Analyze data quickly and efficiently with ATLAS.ti

Download a free trial to see how you can make sense of complex qualitative data.

Numerous fields and sectors conduct descriptive research for its versatile and detailed nature. Through its focus on presenting things as they naturally occur, it provides insights into a myriad of scenarios. Here are some tangible examples from diverse domains:

Conducting market research

Businesses often turn to data analysis through descriptive research to understand the demographics of their target market. For instance, a company launching a new product might survey potential customers to understand their age, gender, income level, and purchasing habits, offering valuable data for targeted marketing strategies.

Evaluating employee behaviors

Organizations rely on descriptive research designs to assess the behavior and attitudes of their employees. By conducting observations or surveys , companies can gather data on workplace satisfaction, collaboration patterns, or the impact of a new office layout on productivity.

Understanding consumer preferences

Brands aiming to understand their consumers' likes and dislikes often use descriptive research. By observing shopping behaviors or conducting product feedback surveys , they can gauge preferences and adjust their offerings accordingly.

Documenting historical patterns

Historians and anthropologists employ descriptive research to identify patterns through analysis of events or cultural practices. For instance, a historian might detail the daily life in a particular era, while an anthropologist might document rituals and ceremonies of a specific tribe.

Assessing student performance

Educational researchers can utilize descriptive studies to understand the effectiveness of teaching methodologies. By observing classrooms or surveying students, they can measure data trends and gauge the impact of a new teaching technique or curriculum on student engagement and performance.

Descriptive research methods aim to authentically represent situations and phenomena. These techniques ensure the collection of comprehensive and reliable data about the subject of interest.

The most appropriate descriptive research method depends on the research question and resources available for your research study.

Surveys and questionnaires

One of the most familiar tools in the researcher's arsenal, surveys and questionnaires offer a structured means of collecting data from a vast audience. Through carefully designed questions, researchers can obtain standardized responses that lend themselves to straightforward comparison and analysis in quantitative and qualitative research .

Survey research can manifest in various formats, from face-to-face interactions and telephone conversations to digital platforms. While surveys can reach a broad audience and generate quantitative data ripe for statistical analysis, they also come with the challenge of potential biases in design and rely heavily on respondent honesty.

Observations and case studies

Direct or participant observation is a method wherein researchers actively watch and document behaviors or events. A researcher might, for instance, observe the dynamics within a classroom or the behaviors of shoppers in a market setting.

Case studies provide an even deeper dive, focusing on a thorough analysis of a specific individual, group, or event. These methods present the advantage of capturing real-time, detailed data, but they might also be time-intensive and can sometimes introduce observer bias .

Interviews and focus groups

Interviews , whether they follow a structured script or flow more organically, are a powerful means to extract detailed insights directly from participants. On the other hand, focus groups gather multiple participants for discussions, aiming to gather diverse and collective opinions on a particular topic or product.

These methods offer the benefit of deep insights and adaptability in data collection . However, they necessitate skilled interviewers, and focus group settings might see individual opinions being influenced by group dynamics.

Document and content analysis

Here, instead of generating new data, researchers examine existing documents or content . This can range from studying historical records and newspapers to analyzing media content or literature.

Analyzing existing content offers the advantage of accessibility and can provide insights over longer time frames. However, the reliability and relevance of the content are paramount, and researchers must approach this method with a discerning eye.

Descriptive research data, rich in details and insights, necessitates meticulous analysis to derive meaningful conclusions. The analysis process transforms raw data into structured findings that can be communicated and acted upon.

Qualitative content analysis

For data collected through interviews , focus groups , observations , or open-ended survey questions , qualitative content analysis is a popular choice. This involves examining non-numerical data to identify patterns, themes, or categories.

By coding responses or observations , researchers can identify recurring elements, making it easier to comprehend larger data sets and draw insights.

Using descriptive statistics

When dealing with quantitative data from surveys or experiments, descriptive statistics are invaluable. Measures such as mean, median, mode, standard deviation, and frequency distributions help summarize data sets, providing a snapshot of the overall patterns.

Graphical representations like histograms, pie charts, or bar graphs can further help in visualizing these statistics.

Coding and categorizing the data

Both qualitative and quantitative data often require coding. Coding involves assigning labels to specific responses or behaviors to group similar segments of data. This categorization aids in identifying patterns, especially in vast data sets.

For instance, responses to open-ended questions in a survey can be coded based on keywords or sentiments, allowing for a more structured analysis.

Visual representation through graphs and charts

Visual aids like graphs, charts, and plots can simplify complex data, making it more accessible and understandable. Whether it's showcasing frequency distributions through histograms or mapping out relationships with networks, visual representations can elucidate trends and patterns effectively.

In the realm of research , the credibility of findings is paramount. Without trustworthiness in the results, even the most meticulously gathered data can lose its value. Two cornerstones that bolster the credibility of research outcomes are validity and reliability .

Validity: Measuring the right thing

Validity addresses the accuracy of the research. It seeks to answer the question: Is the research genuinely measuring what it aims to measure? In descriptive research, where the objective is to paint an authentic picture of the current state of affairs, ensuring validity is crucial.

For instance, if a study aims to understand consumer preferences for a product category, the questions posed should genuinely reflect those preferences and not veer into unrelated territories. Multiple forms of validity, including content, criterion, and construct validity, can be examined to ensure that the research instruments and processes are aligned with the research goals.

Reliability: Consistency in findings

Reliability, on the other hand, pertains to the consistency of the research findings. When a study demonstrates reliability, this suggests that others could repeat the study and the outcomes would remain consistent across repetitions.

In descriptive research, factors like the clarity of survey questions , the training of observers , and the standardization of interview protocols play a role in enhancing reliability. Techniques such as test-retest and internal consistency measurements can be employed to assess and improve reliability.

Make your research happen with ATLAS.ti

Analyze descriptive research with our powerful data analysis interface. Download a free trial of ATLAS.ti.

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence

Market Research

• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Descriptive Research

Try Qualtrics for free

Descriptive research: what it is and how to use it.

8 min read Understanding the who, what and where of a situation or target group is an essential part of effective research and making informed business decisions.

For example you might want to understand what percentage of CEOs have a bachelor’s degree or higher. Or you might want to understand what percentage of low income families receive government support – or what kind of support they receive.

Descriptive research is what will be used in these types of studies.

In this guide we’ll look through the main issues relating to descriptive research to give you a better understanding of what it is, and how and why you can use it.

Free eBook: 2024 global market research trends report

What is descriptive research?

Descriptive research is a research method used to try and determine the characteristics of a population or particular phenomenon.

Using descriptive research you can identify patterns in the characteristics of a group to essentially establish everything you need to understand apart from why something has happened.

Market researchers use descriptive research for a range of commercial purposes to guide key decisions.

For example you could use descriptive research to understand fashion trends in a given city when planning your clothing collection for the year. Using descriptive research you can conduct in depth analysis on the demographic makeup of your target area and use the data analysis to establish buying patterns.

Conducting descriptive research wouldn’t, however, tell you why shoppers are buying a particular type of fashion item.

Descriptive research design

Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis.

As a survey method, descriptive research designs will help researchers identify characteristics in their target market or particular population.

These characteristics in the population sample can be identified, observed and measured to guide decisions.

Descriptive research characteristics

While there are a number of descriptive research methods you can deploy for data collection, descriptive research does have a number of predictable characteristics.

Here are a few of the things to consider:

Measure data trends with statistical outcomes

Descriptive research is often popular for survey research because it generates answers in a statistical form, which makes it easy for researchers to carry out a simple statistical analysis to interpret what the data is saying.

Descriptive research design is ideal for further research

Because the data collection for descriptive research produces statistical outcomes, it can also be used as secondary data for another research study.

Plus, the data collected from descriptive research can be subjected to other types of data analysis .

Uncontrolled variables

A key component of the descriptive research method is that it uses random variables that are not controlled by the researchers. This is because descriptive research aims to understand the natural behavior of the research subject.

It’s carried out in a natural environment

Descriptive research is often carried out in a natural environment. This is because researchers aim to gather data in a natural setting to avoid swaying respondents.

Data can be gathered using survey questions or online surveys.

For example, if you want to understand the fashion trends we mentioned earlier, you would set up a study in which a researcher observes people in the respondent’s natural environment to understand their habits and preferences.

Descriptive research allows for cross sectional study

Because of the nature of descriptive research design and the randomness of the sample group being observed, descriptive research is ideal for cross sectional studies – essentially the demographics of the group can vary widely and your aim is to gain insights from within the group.

This can be highly beneficial when you’re looking to understand the behaviors or preferences of a wider population.

Descriptive research advantages

There are many advantages to using descriptive research, some of them include:

Cost effectiveness

Because the elements needed for descriptive research design are not specific or highly targeted (and occur within the respondent’s natural environment) this type of study is relatively cheap to carry out.

Multiple types of data can be collected

A big advantage of this research type, is that you can use it to collect both quantitative and qualitative data. This means you can use the stats gathered to easily identify underlying patterns in your respondents’ behavior.

Descriptive research disadvantages

Potential reliability issues.

When conducting descriptive research it’s important that the initial survey questions are properly formulated.

If not, it could make the answers unreliable and risk the credibility of your study.

Potential limitations

As we’ve mentioned, descriptive research design is ideal for understanding the what, who or where of a situation or phenomenon.

However, it can’t help you understand the cause or effect of the behavior. This means you’ll need to conduct further research to get a more complete picture of a situation.

Descriptive research methods

Because descriptive research methods include a range of quantitative and qualitative research, there are several research methods you can use.

Use case studies

Case studies in descriptive research involve conducting in-depth and detailed studies in which researchers get a specific person or case to answer questions.

Case studies shouldn’t be used to generate results, rather it should be used to build or establish hypothesis that you can expand into further market research .

For example you could gather detailed data about a specific business phenomenon, and then use this deeper understanding of that specific case.

Use observational methods

This type of study uses qualitative observations to understand human behavior within a particular group.

By understanding how the different demographics respond within your sample you can identify patterns and trends.

As an observational method, descriptive research will not tell you the cause of any particular behaviors, but that could be established with further research.

Use survey research

Surveys are one of the most cost effective ways to gather descriptive data.

An online survey or questionnaire can be used in descriptive studies to gather quantitative information about a particular problem.

Survey research is ideal if you’re using descriptive research as your primary research.

Descriptive research examples

Descriptive research is used for a number of commercial purposes or when organizations need to understand the behaviors or opinions of a population.

One of the biggest examples of descriptive research that is used in every democratic country, is during elections.

Using descriptive research, researchers will use surveys to understand who voters are more likely to choose out of the parties or candidates available.

Using the data provided, researchers can analyze the data to understand what the election result will be.

In a commercial setting, retailers often use descriptive research to figure out trends in shopping and buying decisions.

By gathering information on the habits of shoppers, retailers can get a better understanding of the purchases being made.

Another example that is widely used around the world, is the national census that takes place to understand the population.

The research will provide a more accurate picture of a population’s demographic makeup and help to understand changes over time in areas like population age, health and education level.

Where Qualtrics helps with descriptive research

Whatever type of research you want to carry out, there’s a survey type that will work.

Qualtrics can help you determine the appropriate method and ensure you design a study that will deliver the insights you need.

Our experts can help you with your market research needs , ensuring you get the most out of Qualtrics market research software to design, launch and analyze your data to guide better, more accurate decisions for your organization.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

Ready to learn more about Qualtrics?

Using Science to Inform Educational Practices

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments. The main categories of psychological research are descriptive, correlational, and experimental research. Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions.

Research studies that do not test specific relationships between variables are called  descriptive studies . For this method, the research question or hypothesis can be about a single variable (e.g., How accurate are people’s first impressions?) or can be a broad and exploratory question (e.g., What is it like to be a working mother diagnosed with depression?). The variable of the study is measured and reported without any further relationship analysis. A researcher might choose this method if they only needed to report information, such as a tally, an average, or a list of responses. Descriptive research can answer interesting and important questions, but what it cannot do is answer questions about relationships between variables.

Video 2.4.1.  Descriptive Research Design  provides explanation and examples for quantitative descriptive research. A closed-captioned version of this video is available here .

Descriptive research is distinct from  correlational research , in which researchers formally test whether a relationship exists between two or more variables.  Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about causal relationships between variables. We will discuss each of these methods more in-depth later.

Table 2.4.1. Comparison of research design methods

Candela Citations

• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/adolescent/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Educational Psychology Copyright © 2020 by Nicole Arduini-Van Hoose is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• Descriptive Research Designs: Types, Examples & Methods

One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.

This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.

In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.

What is Descriptive Research?

Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.

This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. 

For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.

What Are The Types of Descriptive Research?

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer. 

This way, the researcher can describe the qualifications possessed by the employed demographics of this community. 

• Descriptive-normative survey

This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.

Examples of Descriptive Research

There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.

• Comparing Student Performance:

An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.

Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.

• Scientific Classification

During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.

For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc. 

All these classifications are made a result of descriptive research which describes what they are.

• Human Behavior

When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.

This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.

What are the Characteristics of Descriptive Research?  

The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:

• Quantitativeness

Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.

• Qualitativeness

It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.

• Uncontrolled variables

In descriptive research, researchers cannot control the variables like they do in experimental research.

• The basis for further research

The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.

This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.

Why Use Descriptive Research Design?  

Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.

• Define subject characteristics :

It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.

For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.

• Measure Data Trends

It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.

Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.

Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?

Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.

• Validate existing conditions

When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.

• Conducted Overtime

Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.

What are the Disadvantages of Descriptive Research?  

• Response and Non-response Bias

Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.

• The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
• A case-study or sample taken from a large population is not representative of the whole population.
• Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

What are the Data Collection Methods in Descriptive Research?  

There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.

1. Observational Method

The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.

It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.

Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods. 

Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.

2. Case Study Method

A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.

This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.

3. Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.

Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.

Survey research can be carried out both online and offline using the following methods

• Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
• Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.

What Are The Differences Between Descriptive and Correlational Research?  

Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.

Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).

Correlational research may be used in 2 situations;

(i) when trying to find out if there is a relationship between two variables, and

(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables. 

Below are some of the differences between correlational and descriptive research:

• Definitions :

Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables. 

• Characteristics :

Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.

• Predictions :

 Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.

Descriptive Research vs. Causal Research

Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation. 

It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.

Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.

Descriptive Research vs. Analytical Research

Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor. 

It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors. 

It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.

Descriptive Research vs. Exploratory Research

Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause. 

Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic. 

It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.

Read More – Exploratory Research: What are its Method & Examples?

Descriptive Research vs. Experimental Research

Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs. 

Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.

Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects. 

Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.

Related – Experimental vs Non-Experimental Research: 15 Key Differences

Descriptive Research vs. Explanatory Research

Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context. 

Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms. 

It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.

Descriptive Research vs. Inferential Research

Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study. 

Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.

Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample. 

It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.

Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .

It is also very useful in solving real-life problems in various fields of social science, physical science, and education.

Connect to Formplus, Get Started Now - It's Free!

• descriptive research
• descriptive research method
• example of descriptive research
• types of descriptive research
• busayo.longe

You may also like:

Type I vs Type II Errors: Causes, Examples & Prevention

This article will discuss the two different types of errors in hypothesis testing and how you can prevent them from occurring in your research

Acceptance Sampling: Meaning, Examples, When to Use

In this post, we will discuss extensively what acceptance sampling is and when it is applied.

Extrapolation in Statistical Research: Definition, Examples, Types, Applications

In this article we’ll look at the different types and characteristics of extrapolation, plus how it contrasts to interpolation.

Cross-Sectional Studies: Types, Pros, Cons & Uses

In this article, we’ll look at what cross-sectional studies are, how it applies to your research and how to use Formplus to collect...

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

Bridging the Gap: Overcome these 7 flaws in descriptive research design

Descriptive research design is a powerful tool used by scientists and researchers to gather information about a particular group or phenomenon. This type of research provides a detailed and accurate picture of the characteristics and behaviors of a particular population or subject. By observing and collecting data on a given topic, descriptive research helps researchers gain a deeper understanding of a specific issue and provides valuable insights that can inform future studies.

In this blog, we will explore the definition, characteristics, and common flaws in descriptive research design, and provide tips on how to avoid these pitfalls to produce high-quality results. Whether you are a seasoned researcher or a student just starting, understanding the fundamentals of descriptive research design is essential to conducting successful scientific studies.

Table of Contents

What Is Descriptive Research Design?

The descriptive research design involves observing and collecting data on a given topic without attempting to infer cause-and-effect relationships. The goal of descriptive research is to provide a comprehensive and accurate picture of the population or phenomenon being studied and to describe the relationships, patterns, and trends that exist within the data.

Descriptive research methods can include surveys, observational studies , and case studies, and the data collected can be qualitative or quantitative . The findings from descriptive research provide valuable insights and inform future research, but do not establish cause-and-effect relationships.

Importance of Descriptive Research in Scientific Studies

1. understanding of a population or phenomenon.

Descriptive research provides a comprehensive picture of the characteristics and behaviors of a particular population or phenomenon, allowing researchers to gain a deeper understanding of the topic.

2. Baseline Information

The information gathered through descriptive research can serve as a baseline for future research and provide a foundation for further studies.

3. Informative Data

Descriptive research can provide valuable information and insights into a particular topic, which can inform future research, policy decisions, and programs.

4. Sampling Validation

Descriptive research can be used to validate sampling methods and to help researchers determine the best approach for their study.

5. Cost Effective

Descriptive research is often less expensive and less time-consuming than other research methods , making it a cost-effective way to gather information about a particular population or phenomenon.

6. Easy to Replicate

Descriptive research is straightforward to replicate, making it a reliable way to gather and compare information from multiple sources.

Key Characteristics of Descriptive Research Design

The primary purpose of descriptive research is to describe the characteristics, behaviors, and attributes of a particular population or phenomenon.

2. Participants and Sampling

Descriptive research studies a particular population or sample that is representative of the larger population being studied. Furthermore, sampling methods can include convenience, stratified, or random sampling.

3. Data Collection Techniques

Descriptive research typically involves the collection of both qualitative and quantitative data through methods such as surveys, observational studies, case studies, or focus groups.

4. Data Analysis

Descriptive research data is analyzed to identify patterns, relationships, and trends within the data. Statistical techniques , such as frequency distributions and descriptive statistics, are commonly used to summarize and describe the data.

5. Focus on Description

Descriptive research is focused on describing and summarizing the characteristics of a particular population or phenomenon. It does not make causal inferences.

6. Non-Experimental

Descriptive research is non-experimental, meaning that the researcher does not manipulate variables or control conditions. The researcher simply observes and collects data on the population or phenomenon being studied.

When Can a Researcher Conduct Descriptive Research?

A researcher can conduct descriptive research in the following situations:

• To better understand a particular population or phenomenon
• To describe the relationships between variables
• To describe patterns and trends
• To validate sampling methods and determine the best approach for a study
• To compare data from multiple sources.

Types of Descriptive Research Design

1. survey research.

Surveys are a type of descriptive research that involves collecting data through self-administered or interviewer-administered questionnaires. Additionally, they can be administered in-person, by mail, or online, and can collect both qualitative and quantitative data.

2. Observational Research

Observational research involves observing and collecting data on a particular population or phenomenon without manipulating variables or controlling conditions. It can be conducted in naturalistic settings or controlled laboratory settings.

3. Case Study Research

Case study research is a type of descriptive research that focuses on a single individual, group, or event. It involves collecting detailed information on the subject through a variety of methods, including interviews, observations, and examination of documents.

4. Focus Group Research

Focus group research involves bringing together a small group of people to discuss a particular topic or product. Furthermore, the group is usually moderated by a researcher and the discussion is recorded for later analysis.

5. Ethnographic Research

Ethnographic research involves conducting detailed observations of a particular culture or community. It is often used to gain a deep understanding of the beliefs, behaviors, and practices of a particular group.

Advantages of Descriptive Research Design

1. provides a comprehensive understanding.

Descriptive research provides a comprehensive picture of the characteristics, behaviors, and attributes of a particular population or phenomenon, which can be useful in informing future research and policy decisions.

2. Non-invasive

Descriptive research is non-invasive and does not manipulate variables or control conditions, making it a suitable method for sensitive or ethical concerns.

3. Flexibility

Descriptive research allows for a wide range of data collection methods , including surveys, observational studies, case studies, and focus groups, making it a flexible and versatile research method.

4. Cost-effective

Descriptive research is often less expensive and less time-consuming than other research methods. Moreover, it gives a cost-effective option to many researchers.

5. Easy to Replicate

Descriptive research is easy to replicate, making it a reliable way to gather and compare information from multiple sources.

6. Informs Future Research

The insights gained from a descriptive research can inform future research and inform policy decisions and programs.

Disadvantages of Descriptive Research Design

1. limited scope.

Descriptive research only provides a snapshot of the current situation and cannot establish cause-and-effect relationships.

2. Dependence on Existing Data

Descriptive research relies on existing data, which may not always be comprehensive or accurate.

3. Lack of Control

Researchers have no control over the variables in descriptive research, which can limit the conclusions that can be drawn.

The researcher’s own biases and preconceptions can influence the interpretation of the data.

5. Lack of Generalizability

Descriptive research findings may not be applicable to other populations or situations.

6. Lack of Depth

Descriptive research provides a surface-level understanding of a phenomenon, rather than a deep understanding.

7. Time-consuming

Descriptive research often requires a large amount of data collection and analysis, which can be time-consuming and resource-intensive.

7 Ways to Avoid Common Flaws While Designing Descriptive Research

1. Clearly define the research question

A clearly defined research question is the foundation of any research study, and it is important to ensure that the question is both specific and relevant to the topic being studied.

2. Choose the appropriate research design

Choosing the appropriate research design for a study is crucial to the success of the study. Moreover, researchers should choose a design that best fits the research question and the type of data needed to answer it.

3. Select a representative sample

Selecting a representative sample is important to ensure that the findings of the study are generalizable to the population being studied. Researchers should use a sampling method that provides a random and representative sample of the population.

4. Use valid and reliable data collection methods

Using valid and reliable data collection methods is important to ensure that the data collected is accurate and can be used to answer the research question. Researchers should choose methods that are appropriate for the study and that can be administered consistently and systematically.

5. Minimize bias

Bias can significantly impact the validity and reliability of research findings.  Furthermore, it is important to minimize bias in all aspects of the study, from the selection of participants to the analysis of data.

6. Ensure adequate sample size

An adequate sample size is important to ensure that the results of the study are statistically significant and can be generalized to the population being studied.

7. Use appropriate data analysis techniques

The appropriate data analysis technique depends on the type of data collected and the research question being asked. Researchers should choose techniques that are appropriate for the data and the question being asked.

Have you worked on descriptive research designs? How was your experience creating a descriptive design? What challenges did you face? Do write to us or leave a comment below and share your insights on descriptive research designs!

extremely very educative

Indeed very educative and useful. Well explained. Thank you

Simple,easy to understand

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Publishing Research
• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

• Industry News
• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

Achieving Research Excellence: Checklist for good research practices

Academia is built on the foundation of trustworthy and high-quality research, supported by the pillars…

• Promoting Research

Plain Language Summary — Communicating your research to bridge the academic-lay gap

Science can be complex, but does that mean it should not be accessible to the…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

As a researcher, what do you consider most when choosing an image manipulation detector?

Child Care and Early Education Research Connections

Descriptive research studies.

Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?

Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from  randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.

A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using  descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.

Descriptive studies have an important role in early care and education research. Studies such as the  National Survey of Early Care and Education  and the  National Household Education Surveys Program  have greatly increased our knowledge of the supply of and demand for child care in the U.S. The  Head Start Family and Child Experiences Survey  and the  Early Childhood Longitudinal Study Program  have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.

Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.

Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).

Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.

Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."

Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.

Limitations:

Descriptive studies cannot be used to establish cause and effect relationships.

Respondents may not be truthful when answering survey questions or may give socially desirable responses.

The choice and wording of questions on a questionnaire may influence the descriptive findings.

Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

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

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

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

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

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons

Margin Size

• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

1.4: Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

• Last updated
• Save as PDF
• Page ID 107148

\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

\( \newcommand{\Span}{\mathrm{span}}\)

\( \newcommand{\id}{\mathrm{id}}\)

\( \newcommand{\kernel}{\mathrm{null}\,}\)

\( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\)

\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\)

\( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

\( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vectorC}[1]{\textbf{#1}} \)

\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

Learning Objectives

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table \(\PageIndex{2}\), are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial comparability among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies— descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey— a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation—is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table \(\PageIndex{3}\).

The results of descriptive research projects are analyzed using descriptive statistics— numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure \(\PageIndex{5}\), where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean, is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure \(\PageIndex{5}\), the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (Figure \(\PageIndex{6}\)), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure \(\PageIndex{6}\) that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode, represents the value that occurs most frequently in the distribution . You can see from Figure \(\PageIndex{6}\) that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Or they may be more spread out away from it, like this:

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure \(\PageIndex{5}\) is 72 – 62 = 10. The standard deviation, symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. 

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure \(\PageIndex{10}\), a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure \(\PageIndex{10}\), the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure \(\PageIndex{10}\) shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure \(\PageIndex{10}\) show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = 0.54 or r = 0.67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –0.30 or r = –0.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = -0.54 is a stronger relationship than r = 0.30, and r = 0.72 is a stronger relationship than r = –0.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure \(\PageIndex{11}\) shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables. It is important, however, to keep in mind that using data to make a prediction is not the same thing as being able to say that one thing causes another. Height and weight, for example, are correlated - but you can't state that one "causes" the other.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that impacts both of the measured variables and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When both of the measured variables are both impacted by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could impact your results, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems. It should be noted that one is also unable to control - or measure - all variables that could impact your results in an experiment - but experiments can lead to conclusions about cause.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the variables of interest cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure \(\PageIndex{17}\).

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

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

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may impact the measured variables in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence (comparability) between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Updates and Edits 

This page was forked and modified on January 14, 2021. Slight language modifications were made and references to "predictor" and "outcome" variables with respect to correlational data were modified. While correlational data can be be used to make predictions, referring to the variables in a correlational study as predictors and outcomes may inappropriately imply a causal relationship. 

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

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

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Korean Med Sci
• v.37(16); 2022 Apr 25

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

The learner identity of adolescents with trajectories of resilience: the role of risk, academic experience, and gender

• Open access
• Published: 22 May 2024

Cite this article

You have full access to this open access article

• Jose Antonio Matías-García   ORCID: orcid.org/0000-0002-3832-3804 1 ,
• Mercedes Cubero   ORCID: orcid.org/0000-0003-0417-4246 1 ,
• Andrés Santamaría   ORCID: orcid.org/0000-0002-1191-9536 1 &
• Miguel Jesús Bascón   ORCID: orcid.org/0000-0003-0226-8943 1  

116 Accesses

Explore all metrics

In at-risk areas of social exclusion, a higher number of adolescents drop out of school. Dropout from compulsory education and early school leaving are associated with unemployment, poverty, and greater health problems, posing a significant threat to the youth’s development and wellbeing. Nevertheless, some students manage to pursue formal education even in high-risk areas, exhibiting resilience. Interwoven between the processes of risk and resilience, the students’ identity development plays a vital role. The present study aimed to analyze the learner identity of students who exhibit a resilience trajectory in areas at risk of social exclusion, and its relation to the degree of risk they face, their academic experience, and their gender. The sample consisted of 132 students from at-risk neighborhoods in Spain who successfully completed compulsory secondary education and continued beyond that level. To measure their academic selves, a modified version of the Twenty Statement Test (TST) was used, which was analyzed using a category system that included four dimensions: organization of the self, emotional valence, plane of action, and thematic reference. The results indicate the participants primarily used personal, positive, evaluative self-descriptions related to the academic world, mostly based on effort. A higher degree of risk was associated to more self-descriptions referring good relationships with others and class attendance, while higher academic experience was associated to more independent selves. The study also found several gender differences. The implications of these findings for research and social intervention in at-risk contexts are discussed.

Similar content being viewed by others

The Role of TikTok in Students’ Health and Wellbeing

The role of school in adolescents’ identity development. a literature review.

Carl Rogers: A Person-Centered Approach

Avoid common mistakes on your manuscript.

Introduction

In 2022, 12.3 million people, which accounts for 26% of the Spanish population, were at risk of poverty and/or social exclusion, according to the European Anti-Poverty Network (EAPN, 2023 ). Social exclusion is a structural and multidimensional process that affects various spheres, including the economic, labor, educational, socio-health, residential, relational, and participatory (Contreras-Montero, 2020 ; Hernández, 2010 ). One of the most significant factors related to social exclusion is the lack of access to formal education. Individuals with limited formal education have reduced access to employment and participation, increased health problems, and are more likely to fall into poverty (Hernández, 2010 ; Moreno, 2015 ).

Dropping out of the educational system is a significant issue in Spain. Early school leaving (ESL) is defined as the percentage of students between 18 and 24 years of age who have not completed and are not currently studying any type of post-compulsory education (Eurostat, 2022 ). In 2022, Spain had a percentage of 13.9%, which is one of the highest rates among European countries ( X̅ = 9.6%) (Eurostat, 2022 ). Students can also drop out without finishing compulsory studies. In the scientific literature, school dropout is attributed to a process of progressive disengagement of students from the educational system (Mena et al., 2010 ).

Students who drop out generally refer to discourses of deficit, to a perceived lack of ability to complete their studies, and place a low value to formal education (Romero & Hernández, 2019 ). These students often encounter multiple risk factors that increase the likelihood of dropping out of formal schooling. Most of these factors are associated with social and economic inequalities, such as low levels of education and poverty in the family (Bayón-Calvo et al., 2021 ; EAPN, 2023 ; Huisman & Smits, 2015 ). Conflicting relationships with teachers and/or family members (Romero & Hernández, 2019 ) are also related.

In addition, gender is considered a significant risk factor in the literature. Male students tend to drop out of school more frequently than their female counterparts (Eurostat, 2022 ). This trend may be attributed to gender roles, as men often face greater pressure to enter the labor market (Bayón-Calvo et al., 2021 ; Ruiz et al., 2013 ). Additionally, male gender socialization can make it challenging for adolescents to integrate into school, as it is sometimes incompatible with maintaining status within their group of friends (Reay, 2018 ). Males exhibit more behavioral problems than females, which can impede their integration into school and their relationship with teachers (Fortin et al., 2010 ).

In institutional and organizational contexts, factors such as school segregation both between and within schools (Save the Children, 2019 ) and the higher rate of teacher turnover in areas at risk of social exclusion (Allen et al., 2018 ; Llorent-Bedmar et al., 2021 ) hinder students’ school attainment. The presence of more risk factors increases the likelihood of students dropping out of school (Rouse et al., 2020 ). Therefore, areas at risk of social exclusion are the most affected. We can consider that social class and inequalities are at the root of students’ school attainment and success (Save the Children, 2019 ).

However, even in areas of higher risk, some students complete their compulsory studies and continue with further formal schooling. The concept of resilience helps define these situations for study. Resilience refers to the process of successfully coping or recovering in the face of adverse situations (Gartland et al., 2019 ; Toland & Carrigan, 2011 ). It comprises two elements: significant exposure to severe risk and evidence of positive adaptation despite such risk (Masten, 2007 ). In the educational context, positive adaptation refers to a student’s ability to perform adequately in school, achieving similar or better results than the normative population that does not face risk (Masten et al., 2006 ; Lessard et al., 2014 ; Longás et al., 2019 ).

It is essential to emphasize that resilience should be understood as an interactive and dynamic process that involves individuals, families, schools, and communities (Gartland et al., 2019 ; Toland & Carrigan, 2011 ). It is not an ability or a characteristic of individuals, and does not make people invulnerable, even if achieved in a specific context under certain conditions. If the conditions are altered, individuals who exhibit resilience may no longer demonstrate it. In this sense, the concept of resilience constitutes a definition of situations of positive adaptation in the face of adversity, and not an explanation of such adaptation (Matías-García, 2022 ).

Precisely, for resilience to occur, risk factors must be controlled by protective factors, defined as such because they allow or facilitate positive adaptation in individuals (Gartland et al., 2019 ). Protective factors against ESL in situations of social exclusion may include greater public spending on scholarships and grants (Alegre & Benito, 2010 ), opportunities for family participation in school (Borman & Overman, 2004 ), and the formation of supportive relationships with family, teachers, and/or other students (Lessard et al., 2014 ; Longás et al., 2019 ), among others (Matías-García, 2022 ; Matias-Garcia et al., 2024 ). Protective factors enable students to establish positive academic expectations for the future, engage more with school, and develop an internal locus of control (Lessard et al., 2014 ; Longás et al., 2019 ), which helps them better adapt to the school environment.

Interwoven between the processes of academic risk and resilience, the development of students’ learner identity plays a vital role. According to Lawson ( 2014 ), learner identity is defined as “…how an individual feels about himself/herself as a learner and the extent to which he/she describes himself/herself as a ‘learner’” (p. 344). These identity constructs are known to mediate achievement, particularly in students with low socioeconomic status (Li et al., 2020 ; Hansen & Henderson, 2019 ).

From a cultural psychology theoretical background, our approach to identity involves the study of self or self-construal, which refers to an individual’s sense of self in relation to others (self-description or self-view) (Sedikides & Brewer, 2001 ). From our perspective, cognition is situated in activity settings (Werstch, 1991 ). Thus, we understand the self as a dynamic, distributed, and dialogical entity, and not as stable and homogeneous. For instance, Bruner ( 1996 ) proposed the existence of a distributed self, which he described as a “swarm” of participations that arise from the situations in which an individual participates. While constructing their identity as an individual distinct from others in each sociocultural setting, the person maintains a close relationship with them.

In this sense, the literature shows that students internalize the positive or negative academic discourses from their interpersonal relationships with teachers, peers, and family members, impacting their identity as learners (Matías-García, 2022 ; Matias-Garcia et al., 2024 ; McFarland et al., 2016 ). In the educational contexts, the influence of academic activities and discourses has a particularly high effect on its development. Characteristics such as the ability to learn or to be attentive during classes are constantly evaluated throughout formal schooling, explicitly and individually. These tasks and discourses shape students’ identity construction towards teachers’ demands, in a positive or negative way.

Also, there is extensive evidence from cross-cultural research about the relationship between formal schooling and changes in cognitive processes (Cole, 1996 ; Rogoff, 1990 ; Scribner & Cole, 1981 ), such as the development of abstract, decontextualized verbal thinking. Due to the type of discourse employed in formal instructional contexts, the literature relates formal schooling to the development of more individual, autonomous, reflective, and agentive selves (De la Mata et al., 2015 ; 2019 ). Authors such as Kagitcibasi ( 2007 ), Greenfield ( 2009 ), and Keller ( 2007 ) claim that formal schooling is an important factor that fosters a cultural model of the self that is characterized by autonomy and agency.

Through educational institutions and interactions with others, between risk and protective factors, students develop a unique understanding of themselves, others, and their environment. This understanding allows them to make the identitarian decision to continue with their studies. Thus, the aim of the present work is to describe and analyze the development of the learner identity of students who present a trajectory of resilience in contexts at risk of social exclusion. This will be done so through variables such as the degree of general risk to which they are exposed to, the educational level attained (academic year), or their gender.

In line with Agenda 2030's Sustainable Development Goals current concerns on No Poverty, Education, Inclusion, and Peace and Justice (SDG 1, 4, 11, and 16; United Nations, n.d. ), this study will help us gain the necessary knowledge for the development of psychoeducational interventions that favor educational continuity in students facing social exclusion. Therefore, the present research presents the following exploratory objectives:

To analyze the characteristics of the learner identity of students with resilience trajectories in contexts at risk of social exclusion.

To compare the learner identity of students from two high schools that present different degrees of risk for dropping out.

To study the role of academic experience (in terms of participants’ academic year) in the development of learner identity.

To investigate the impact of gender on the development of learner identity.

Participants

An a priori power analysis to estimate the sample size required for Mann-Whitney U tests, based on data from Santamaría et al. ( 2010 ), was conducted. This study employed the same instrument and similar analytical categories as the one used in the present work and reported moderate to large effect sizes, so a moderate effect size according to Cohen’s ( 1988 ) criteria was established for the estimation ( d  = 0.5). With an alpha of 0.05 (two-tailed) and a statistical power of 0.80, the sample size required for detecting this effect size is approximately N  = 134.

Rates of social exclusion are higher in the southern half of Spain (Bayón-Calvo et al., 2021 ; EAPN, 2023 ), which constituted the focus of this research. The high schools from areas at risk of social exclusion in Seville (Andalusia) were sampled. These areas are labelled as Zones in Need of Social Transformation (ZNST) by local administrators. We used purposeful sampling and selected two high schools from different neighborhoods, which, although both were in ZNST, differed in the degree of risk they presented. According to their principals and teachers, High School A presented a higher proportion of migrant students and ethnic minorities (i.e. gypsy population), a higher rate of conflict, and greater deterioration of the school facilities and the neighborhood. Likewise, all the students in the High School A come from the same neighborhood in which the school is located, which according to City Hall of Seville ( 2017 ) has the highest rate of absenteeism in the city. According to the literature, these variables are associated to increased risk for dropping out (Cordero et al., 2014 ; Mena et al., 2010 ; Romero & Hernández, 2019 ). However, the students from High School B come both from the high school’s neighborhood and from other surrounding neighborhoods of lower risk, providing a different profile of school. More differences between both high schools can be seen in the “ Results ” section, where sociodemographic data is presented.

All students from post-compulsory secondary education ( Bachillerato , ISCED Level 3) from the aforementioned high schools were studied. In Spain, this academic course is comprised of 2 years and traditionally prepares the students for accessing university studies. Following Masten ( 2007 )’s definition, we considered these students presented a resilience trajectory. They completed compulsory education and continue in the education system beyond that level, showing proper academic performance in the face of risk (Lessard et al., 2014 ; Longás et al., 2019 ). Students from vocational training ( Grado Medio , ISCED Level 3) could also fall within this definition but were not the focus of the present research. The final sample was comprised of 132 Bachillerato students (statistical power = 0.795).

The variables considered for the study were high school, academic year, and gender (Table  1 ). The mean age of the participants was 17.43. Concerning the students’ educational gap, 21.4% of them were older than the age expected for their grade level, with a gap from 1 to 7 years. In the most extreme cases, these students had dropped out of school and returned several years later.

Many of the students came from single-parent (21.9%), extended (15.1%), or reunited (3.8%) families. In general, they belonged to large families, with an average of 3.82 persons in the household. A mean of 1.37 people in the family had an income, which essentially came from low-skilled or temporary jobs. 44.7% were low-skilled or unskilled, unstable jobs; 25.2% were in medium-skilled jobs or which involved merit selection or passing an exam, while only 3.5% were in high-skilled jobs. Also, 24.8% were unemployed or housewives and 1.7% were retired or pensioned. Finally, parents’ formal education was predominantly low, with 11.8% of the parents having no formal education, 41.4% having basic education (primary or secondary school), 33.8% having intermediate education (post-compulsory secondary school or vocational training), and 12.9% having higher education (technical college or university).

Instruments

A sociodemographic questionnaire and, as a self-report measure of learner identity, a modified version of the Twenty Statement Test (TST) (Kuhn & McPartland, 1954 ; Santamaría et al., 2010 ; Santamaría & Cubero, 2019 , among others) was applied. The original version consisted of 20 sentences beginning with “I am.” The participants were asked to complete them with adjectives, nouns, or short phrases describing what they thought of themselves, or what others thought of them, whether positive or negative. The TST does not provide a priori information like other questionnaires or Likert-type scales, and therefore has the advantage of a more qualitative and flexible approach to the dimensions of meaning individuals construct about themselves. Based on previous research from the literature, some modifications to the instrument were made in order to adapt it to the goals and the context of assessment:

Reducing the 20 items to 10. Previous studies have shown participants have difficulty in completing all 20 sentences (Santamaría et al., 2010 ).

To specifically ask their characteristics as a student to study their learner identity.

Changing the “I am” (“Yo soy”) item structure for only “I” (“Yo”). The English “I am” may be translated by two different verbs in Spanish, either “ ser ” or “ estar ,” which offers a richer range of possible answers than its direct translation in Spanish, “Yo soy.” This change enabled a similar range of answers for the Spanish population.

The instructions specified that answers could also include membership to groups or personal relationships with others. The reason was to avoid the individualistic bias shown by previous studies (Santamaría et al., 2010 ), since these elements tend to be underrepresented.

G*power ver. 3.1 and SPSS ver. 25 were used for calculating the a priori estimation of the sample and analyzing the data, respectively.

Approval was obtained from the local ethics committee (Granting Body: Portal de Ética de la Investigación Biomédica de Andalucía; Protocol Number: 0191-N-18). For the selection of participants, we obtained research approval and the support of the local education administration. Then, the high schools were contacted. Informed consent was obtained from parents and students over 18 years old. The dates for the data gathering sessions were agreed with the teachers from each high school.

A pilot study was carried out in the same high schools and classrooms to ensure that the instructions were understood and that there were no problems with the TST and sociodemographic questionnaire. Some modifications were done to the latter to facilitate its completion and avoid missing data. The definitive data were collected by researchers from the research group over 2 years, applying the TST to all post-compulsory students at both high schools. The instruments were completed in writing and during class time, after a brief explanation of the research and the procedure to be followed. The students took approximately 15 to 20 min to complete them. The students did not receive financial compensation for their participation.

Data analysis

A category system was applied to analyze the self-descriptions that students provided in the TST. Each statement was categorized under four different dimensions. These were organization of the self, emotional valence, plane of action, and thematic reference (Table 2 ). For the reliability analysis of each dimension, 20% of participants were randomly selected from the total sample and their self-descriptions as students were independently coded by two researchers, authors of the present work. Subsequently, the kappa index was calculated as a measure of inter-rater reliability (Cohen, 1960 ).

The first three dimensions were conformed a priori and have already been used in the literature. The organization of the self dimension is based on the distinction of Triandis ( 1989 ) between the private, public, and collective self (Santamaría et al., 2010 ; Yuste et al., 2021 ). A second dimension focuses on the emotional valence of the participants’ description of the self, distinguishing between positive, negative, and ambivalent (Santamaría & Cubero, 2019 ). The third dimension analyzes the plane of actions of self-descriptions. It moves in relation to the trichotomy descriptions, actions, and evaluations (Yuste et al., 2021 ). These dimensions showed good reliability (Table 2 ) according to Cohen ( 1960 ).

Finally, an inductive thematic analysis of the self-descriptions was developed, based on the participants’ answers (Clarke et al., 2015 ). Seventy-six different categories were iteratively elaborated a posteriori, which were very similar to the descriptions that the participants themselves filled out in the TST. Subsequently, they were recategorized into hierarchically higher categories, until five final general categories were created: academic world, social relations, ways of being, work, and finally, leisure and other activities (Matías-García et al., 2023 ). Given the interest in academics, data are also presented for the five subcategories that form Academic World at its previous level (Table 3 ). For reasons of space, microlevel categories will be presented only if they show statistical differences. The kappa index was calculated from the micro level, from 76 different categories, giving a value of 0.866, considered a very good value (Cohen, 1960 ).

Chi-squared ( X 2 ), Fishers’ exact, and Mann-Whitney  U tests were used depending on data type. Percentage of appearance of TST categories was used for analysis. Cramer’s V and Rosenthal’s r were calculated as effect size measures and interpreted according to Cohen ( 1988 ).

First, the descriptive data obtained from the TST will be shown. Subsequently, data referring to the comparison between the different explanatory variables, school, academic year, and gender will be presented.

Descriptive data

The participants’ self-descriptions were mainly personal, positive, evaluative, and referred to the academic world (Table 4 ). The organization of the self shows a small percentage of relational categories and minimal group categories, while in the emotional valence, negative valence is found in one-third of the responses and ambivalent in one-tenth. In the plane of action, very few actions and even fewer descriptions were categorized. Finally, in the thematic reference dimension, in addition to the high percentage referring to the academic world, we found some categories related to social relations and ways of being, and very few referring to work or leisure.

Within the academic world, the highest percentage of self-descriptions are those referring to the abilities and activities of the students. The next most numerous category would be academic emotions. Finally, with less than 5%, we find the categories global conception, academic relationships, and academic goals.

Differences in terms of the explanatory variables high school, academic year, and gender

High school.

First, further differences will be presented between the two high schools to help contextualize and interpret data, based on the analysis of the sociodemographic questionnaire. Subsequently, the differences referred to the TST will be shown.

High School A had a lower number of students who reached the second year of post-compulsory education (34.1%) than High School B (52.3%), X 2 (1, N  = 132) = 3.9, p  = .048, V  = 0.17). Likewise, High School A’s students are significantly older ( M  = 18.3, DT  = 1.71), U  = 985, p  < .001, r  = .41. In terms of family configuration, High School A also showed a higher percentage of extended family (27.3%) than High School B (9.1%), X 2 (1, N  = 132) = 7.54, p  = .006, V  = 0.24). Father’s ( M  = 1.93, SD  = 0.73) and mother’s ( M  = 1.97, SD  = 0.67) educational levels were also lower relative to High School B ( M  = 2.82, SD  = 0.95 and M  = 2.61, SD  = 0.71, respectively), U  = 500, p  < .001, r  = .43 y U  = 740, p  < .001, r  = .38, respectively. The parents’ job analysis also revealed significant socioeconomic differences. Students at High School A came from families whose parents had mostly low-level jobs (81.5%). Few had medium-level jobs (11.1%) and there were no parents with high-level jobs. Finally, 7.4% were unemployed. On the other hand, at High School B, their families had mostly medium level jobs (53.6%). A lower percentage had low-level jobs (32.1%), and few had high level jobs (1.8%). Finally, 1.8% were retired and/or pensioned and 10.7% were unemployed. These differences were significant ( p  < .001, Fisher’s exact test, V  = 0.47). All these data confirmed that High School A presents a higher degree of risk for dropout than High School B.

Data referring to the analysis of self-descriptions as a function of the high schools will be next presented (Table 5 ). High School A has a significantly higher percentage of unspecific relational and positive self-descriptions. There also were more self-descriptions related to academic relationships and more social (non-academic) relationships.

On the other hand, High School B presents a higher percentage of personal self-descriptions and of ambivalent emotional valence, and referred more to the academic world. Within the academic world, we also find more self-descriptions related to abilities and activities and to academic emotions in High School B. No significant differences were found in the plane of action.

In addition, there were significant differences related to the micro-level categories that make up the thematic reference analysis (Table 6 ). For reasons of space, only statistically significant categories are included.

Thus, we find that students from High School A use more self-descriptions categorized as referring to supportive and helpful relationships with classmates, high non-academic relational abilities, and high attendance and punctuality. However, they also present more self-descriptions of overall poor student conception and other people’s (perceived as) negative academic motivations, in which participants feel pressured or obligated to perform their studies. On the other hand, students from High School B use more self-descriptions that allude to their capacity for effort and organization as a student, either low or high, and to high intelligence. However, they present more descriptions referring to low study and learning and to negative academic emotions, such as anxiety or felling overwhelmed.

All significant differences related to their high school have a small-moderate effect size.

Academic year

Relating to organization of the self, second-year students presented a significantly higher percentage of personal self-descriptions ( M  = 86.2, SD  = 12.3) than first-year students ( M  = 75.6, SD  = 20.4), U  = 1548, p  = .004, with a small effect size ( r  = .25). No significant differences were found for emotional valence. In the plane of action, there were less descriptive self-descriptions in second-year students ( M  = 0, SD  = 0) than in first-year students ( M  = 1.65, SD  = 6.81), U  = 1983, p  = .021, with a small effect size ( r  = .2). In the thematic reference, second-year participants presented a higher percentage of academic world categories ( M  = 88.6, SD  = 14.2) than the first-year students ( M  = 75, SD  = 25.7), U  = 1477, p  = .001, with a small effect size ( r  = .28).

Likewise, within the academic world, second-year students used more self-descriptions referring to abilities and activities ( M  = 64.4, SD  = 19.2) than first-year students ( M  = 52.2, SD  = 23.2), U  = 1526, p  = .003, with a small effect size ( r  = .25). On the other hand, second-year students presented less self-descriptions related to social relations ( M  = 3.55, SD  = 6.66) than the first-year students ( M  = 13.9, SD  = 17.1), U  = 1402, p  < .001, with a small effect size ( r  = .34).

In addition, there also were significant differences related to micro-level categories (Table 7 ). The second-year students presented more self-descriptions related to their capacity for effort and organization, whether low or high, high participation in class, and positive academic emotions, such as satisfaction with their studies.

However, they presented a higher proportion of categories related to low attention in class. As for the first-year students, they presented a greater number of categories related to their behavior in class, either behaving calmly or nervously in class, non-academic relational skills, and high creativity. All these significant differences have small to moderate effect sizes.

No significant gender differences were found in organization of the self, emotional valence, or plane of action. In thematic reference, female students presented more academic world categories ( M  = 84.9, SD  = 21.6) than males ( M  = 78.4, SD  = 22.4), U  = 1612, p  = .011, with a small effect size ( r  = .22). However, males presented more ways of being categories ( M  = 9.7, SD  = 14.5) than females ( M  = 6.03, SD  = 12.2), U  = 1740, p  = .032, with a small effect size ( r  = .19).

In the micro-level analysis, there also were statistical differences (Table 8 ). Female students presented a greater number of self-descriptions referring to a high capacity for effort and organization. However, they presented more self-descriptions referring to a low ability to pass or get a good grade, as well as more self-descriptions referring to both academic optimism and pessimism. On the other hand, male students had more self-descriptions referring to support and help among classmates, calm classroom behavior, and high creativity. All these differences showed small effect sizes.

The present study analyzed the learner identity of students presenting a trajectory of educational resilience in contexts at risk of social exclusion (Lessard et al., 2014 , Longás, 2019 ; Toland & Carrigan, 2011 ). For this purpose, the participants’ self-descriptions as students were studied through the TST. Understanding how these students elaborate their learner identity allows us to better understand educational resilience in these contexts.

Students who drop out generally allude to a lack of ability, motivation, and social value of school (Romero & Hernández, 2019 ). Leaving the education system is viewed as a success rather than a failure by these students, as they believe they can stop wasting time and engage in more productive activities (Mena et al., 2010 ). However, when asked about their learner identity, the study’s participants employed mainly personal, positive, evaluative self-descriptions, linked to the academic world. These descriptions are associated with higher academic performance and mastery-focused academic goals (Hansen & Henderson, 2019 ; Li et al., 2020 ).

In addition, the most common micro-level category was effort in academic tasks, whether high or low (see Tables  6  or 7 ). For the participants, making an effort is one of their primary characteristics as learners. In this sense, incremental beliefs of one’s own abilities, that is, beliefs that one’s ability and intelligence can be improved and developed through effort, have been related to greater motivation, academic performance, task mastery goals, persistence in the face of failure, and greater academic resilience, especially in students at risk of social exclusion (Burnette et al., 2013 ; Matías-García & Cubero-Pérez, 2021 ; Sarrasin et al., 2018 ). Likewise, interventions on these beliefs have been shown to be especially beneficial to at-risk students (Sarrasin et al., 2018 ). Explaining successes and failures in terms of effort has the advantage of making academic difficulties controllable, which gives them agency over their own life trajectory (Burnette et al., 2013 ; Skinner & Zimmer-Gembeck, 2011 ). This alternative to deficit discourses in at-risk students who show a trajectory of resilience is consistent with previous research in the literature (Cubero-Pérez et al., 2023 ; Matias-Garcia et al., 2024 ).

Thus, identity serves as an attributional resource that explains the students’ successes and failures. Despite being in ZNST, these students identify themselves as students who make an effort and are capable and participative in academic settings. This is identitarily coherent with pursuing further studies, allowing them to make such a decision. The importance of building positive learner identities has also been highlighted by other authors (Rosales & Cubero, 2016 ) and their development depends on both the context and the students’ interactions with significant others, such as families, teachers, and classmates (Matías-García, 2022 ; Matias-Garcia et al., 2024 ; McFarland et al., 2016 ). The next section will discuss the impact of risk, academic experience, and gender on the development of learner identity.

Although both high schools are located in contexts at risk of social exclusion, they differed in the degree of risk they presented. High School A had older students, resulting in a greater educational gap. Its students only came from ZNST and had families with a significantly lower socioeconomic status. Additionally, High School A had a higher number of migrant students, a higher rate of conflict, and greater facility deterioration.

In High School A, there was a higher frequency of self-descriptions related to supporting and helping classmates as well as attending class. In contrast, High School B had more self-descriptions related to abilities and activities. This suggests that, even though both high schools are in ZNST, the learning relationships and interactions between teachers and students that occur within them are different (McFarland et al., 2016 ). The higher rate of conflict and absenteeism in High School A indicates that one of the main tasks of its teachers is to ensure that students attend class and maintain good student behavior, in terms of them staying quiet during lessons and not provoke fights with others. Thus, those High School A students who do not fail and who remain in the educational system highlight to a greater extent their good behavior and attendance as something important in their role as students. They elaborate their learner identity in relation to others (Sedikides & Brewer, 2001 ), as a way of differentiating themselves from more troubling students. In contrast, in High School B, the students focused more on their own abilities as students (study, intelligence, effort, organization, etc.), characteristics that education usually values highly in the absence of other difficulties.

According to the data provided by the TST, the students from High School A had the most interdependent selves, based on supportive relationships among peers and friends. On the other hand, the High School B students’ selves were more personal and associated with education. They also presented a more ambivalent and less positive emotional valence, showing a certain greater degree of reflection on their own self-descriptions (De la Mata et al., 2015 ).

These differences are coherent with cross-cultural research. The literature on self-construals advocates a broad distinction between independent and interdependent selves (De la Mata et al., 2015 ; 2019 ; Greenfield, 2009 ; Kitayama et al., 2007 ). People with an independent self-construal view the self as unique, private, and autonomous, whereas people with an interdependent self-construal view the self as related to others, incorporating and referencing the views of other people and groups in their identity. Interdependent selves are related to rural societies and low levels of schooling, whereas independent selves are associated with urban societies which has a greater role for school (De la Mata et al., 2015 ; 2019 ; Greenfield, 2009 ; Kitayama et al., 2007 ). In our sample, the parents from High School A had a lower level of formal education than those from High School B families.

The idea that educational experience fosters the development of independent selves is supported by the significant differences observed between first- and second-year students (Matías-García et al. 2023 ). Second-year students, who are more acclimated to the school environment (Mena et al., 2010 ) and have more educational experience than first-year students, also exhibited more personal self-descriptions and greater association with the academic world (effort, organization, participation, emotions) than first-year students. In contrast, first-year students referenced their social relationships and behavior in class more frequently than second-year students. The educational discourse in class interaction influenced the development of students’ identities, leading to increasingly independent selves associated with school characteristics (De la Mata et al., 2015 ; 2019 ). Matías-García et al. ( 2023 ) show how this process takes place in the development of future selves as well.

However, it is important to note that both profiles of learner identities in the sample, independent or interdependent, demonstrate a trajectory of resilience in their respective high schools and are strongly associated with academic selves. Therefore, it is important to emphasize the relevance of interdependent academic identities in the especially disadvantaged schools and neighborhoods, such as High School A. Cultivating such identities, as well as building good relationships among students and with teachers, could be effective ways to achieve good school integration of students at risk of social exclusion (Longás et al., 2019 ).

In fact, the results of High School A show that fostering an autonomous self does not necessarily imply the undermining of relational selves (Santamaría et al., 2010 ). In contrast to the consideration of two major models of the self, the independent and the interdependent self (Markus & Kitayama, 1991 ), there are authors such as Kagitcibasi ( 2007 ) who reject the traditional consideration of autonomy as opposed to relatedness, which has been predominantly assumed in psychology and in Western thought in general. It is precisely in the specific field of education that this idea has been reinforced in recent years, pointing out that formal education should not only promote aspects of rationality, but also relationality.

Regarding gender, research shows that dropout rates are higher in males than in females (Eurostat, 2022 ). In the present study’s data, female students presented more school-associated identities than male students, indicating greater adaptation and integration in the educational system. Qualitative research suggests that working-class male students may face challenges in maintaining their status within their peer group while integrating into school due to male gender roles (Reay, 2018 ). As a result, they may be more reticent than female students to identify with academic selves. Although female students have more academic success (Eurostat, 2022 ) and display positive self-descriptions regarding their school performance (such as high capacity for effort and organization), they also exhibited more categories of academic pessimism and about obtaining poor grades. In this sense, the literature demonstrates that female students experience greater pressure in school, particularly in certain areas (Kim et al., 2018 ).

In turn, male students presented fewer academic categories and more categories of calm behavior in class, while male students traditionally manifest more behavioral problems (Moreno et al., 2012 ). As mentioned, students who exhibit resilience and do not experience such issues, or at least experience them to a lesser extent, tend to identify these characteristics of themselves as relevant to their identity as a student.

Limitations

Regarding limitations, this research understood resilience as the completion of secondary education and its maintenance in the educational system as a goal in itself. However, the students from this research are still at risk of ESL. Economic needs and other factors may still force them out of the educational system (Mena et al., 2010 , Romero & Hernández, 2019 ). Therefore, measuring other academic adaptation indices, such as the number of passed subjects or the completion of post-compulsory studies, could complement the results. In addition to academic achievement, mental health indicators could be further analyzed in this population. Some students might progress with high levels of stress, discomfort, or “suffering great pressure,” which could also be a problem to address.

Also, it would be worthwhile to analyze the relationship between the students’ learner identity and other non-academic characteristics, such as emotional and affective development, or the social relationships they have. These students face a multitude of difficulties, both in the school context and in the family or peer context, which are interrelated (Toland & Carrigan, 2011 ).

Finally, our results have explanatory value only for students from the post-compulsory bachillerato , a course that traditionally prepares them for university studies. Our sample did not include students from different academically resilient trajectories, such as students from vocational training Grado Medio . These students might develop different types of learner identities, which should be further explored in future research.

Conclusions

This study examined the characteristics of the learner identity of students who exhibit a resilient trajectory. These characteristics align with the healthy learner identity construct found in the literature (Rosales & Cubero, 2016 ). Through their participation in class and interaction with teachers and other students, they elaborated their learner identity in a positive way, while being connected to others. They rejected deficit discourses about the self, common in areas at risk of social exclusion, and built an agentic learner identity mostly based on effort.

The identities constructed by the participants are influenced by the degree of risk present in their high school (and neighborhood), gender, and educational experience. These identities may be more independent or interdependent, more focused on behavior and attendance or on capabilities as learners. In one way or another, all of these students remain resiliently in the educational system beyond the compulsory.

The identification of these characteristics adds necessary information for intervening in ZNST, aligned with national, European, and international goals (Agenda 2030's Sustainable Development Goals; United Nations, n.d. ). The school, in conjunction with families, should promote the construction of positive learner identities attending to the emotional needs that students face when navigating the various challenges presented by their school and context. Identifying and reinforcing characteristics such as effort, attendance, behaving calmly during lessons, being supportive to other students, or the overall conception of being a good student can help build identities consistent with remaining in the educational system. This is especially important for ZNST students, as it can reduce feelings of disengagement and promote adaptive behaviors and persistence.

Alegre, M. Á., & Benito, R. (2010). Los factores del abandono educativo temprano. España en el marco europeo [Factors in early school dropout. Spain in the European framework]. Revista de Educación, extra issue, 65–92. https://hdl.handle.net/20.500.12799/1208

Allen, R., Burgess, S., & Mayo, J. (2018). The teacher labour market, teacher turnover and disadvantaged schools: New evidence for England. Education Economics , 26 (1), 4–23. https://doi.org/10.1080/09645292.2017.1366425

Article   Google Scholar  

Bayón-Calvo, S., Lucas‐García, J., & Gómez‐García, R. (2021). An endogenous analysis of early school leaving in Spain: The role of gender and regional inequalities. European Journal of Education , 56 (3), 482–495. https://doi.org/10.1111/ejed.12461

Borman, G. D., & Overman, L. T. (2004). Academic resilience in mathematics among poor and minority students. The Elementary School Journal, 104 , 177–195. https://doi.org/10.1086/499748

Bruner, J. (1996). Frames for thinking: Ways of making meanings. In D. Olson, & N. Torrance (Eds.), Modes of thought: Explorations in culture and cognition (pp. 95–105). Cambridge University Press.

Google Scholar  

Burnette, J. L., O’Boyle, E. H., VanEpps, E. M., Pollack, J. M., & Finkel, E. J. (2013). Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin , 139 (3), 655–701. https://doi.org/10.1037/a0029531

City Hall of Seville. (2017). Estudio de Necesidades Sociales de Barrios de Sevilla 2017 [Study of social needs in Seville’s neighbourhoods 2017] . Ayuntamiento de Sevilla.

Clarke, V., Braun, V., & Hayfield, N. (2015). Thematic analysis. In J. Smith (Ed.), Qualitative psychology: A practical guide to research methods (pp. 222–248). Sage.

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement , 20 (1), 37–46. https://doi.org/10.1177/001316446002000104

Cohen, J. (1988). Statistical power analysis for the behavioral sciences . Routledge Academic.

Cole, M. (1996). Cultural psychology: A once and future discipline . Harvard University Press.

Contreras-Montero, B. (2020). Una revisión del concepto de exclusión social y su aplicación a la sociedad española tras la crisis económica mundial. Una visión de proceso [A review of the concept of social exclusion and its application to Spanish society in the aftermath of the global economic crisis. A process visión]. Trabajo Social Global – Global Social Work , 10 (19), 3–24. https://doi.org/10.30827/tsg-gsw.v10i19.13582

Cordero, J. M., Manchón, C., & Simancas, R. (2014). Repetition and explanatory factors in Spain. Revista De Educación , 365 , 12–37. https://doi.org/10.4438/1988-592X-RE-2014-365-263

Cubero-Pérez, R., Cubero, M., Bascón, M., & Matías-García, J. A. (2023). Learner identity in secondary post-compulsory education students from areas in need of social transformation: An example of resilience. European Journal of Psychology of Education, 0 (0), 1–22. https://doi.org/10.1007/s10212-023-00704-6

De la Mata, M. L., Santamaría, A., & Cubero, M. (2015). Formal schooling and self construction: A historical-cultural narrative approach. In R. F.d. Castro & B. Selau (Eds.), Cultural-historical approach: Educational research in different contexts (pp. 99–120). EDIPUCRS.

De la Mata, M. L., Santamaría, A., Trigo, E., Cubero, M., Arias, S., Antaliková, R., Hansen, T. G. B., & Ruiz, M. (2019). The relationship between sociocultural factors and autobiographical memories from childhood: The role of formal schooling. Memory, 27 (1), 103–114. https://doi.org/10.1080/09658211.2018.1515316

EAPN (2023). El estado de la pobreza en España. Seguimiento de los indicadores de la Agenda UE 2030. 2015–2022. 13º Informe 2023 [The state of poverty in Spain. Monitoring of the EU 2030 Agenda indicators. 2015–2022. 13th Report 2023]. https://www.eapn.es/ARCHIVO/documentos/documentos/1698046532_informe_arope-2023_completo.pdf

Eurostat (2022). Early leavers from education and training. European Union.  https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Early_leavers_from_education_and_training

Fortin, L., Lessard, A., & Marcotte, D. (2010). Comparison by gender of students with behavior problems who dropped out of school. Procedia - Social and Behavioral Sciences , 2 (2), 5530–5538. https://doi.org/10.1016/j.sbspro.2010.03.902

Gartland, D., Riggs, E., Muyeen, S., Giallo, R., Afifi, T. O., Macmillan, H., & Brown, S. J. (2019). What factors are associated with resilient outcomes in children exposed to social adversity? A systematic review. British Medical Journal Open , 9 (4), 1–14. https://doi.org/10.1136/bmjopen-2018-024870

Greenfield, P. M. (2009). Linking social change and developmental change: Shifting pathways of human development. Developmental Psychology , 45 , 401–418. https://doi.org/10.1037/a0014726

Hansen, K., & Henderson, M. (2019). Does academic self-concept drive academic achievement? Oxford Review of Education , 45 (5), 657–672. https://doi.org/10.1080/03054985.2019.1594748

Hernández, M. (2010). El Estudio De La Pobreza Y La exclusión social. Aproximación cuantitativa y cualitativa [The study of poverty and social exclusion. Quantitative and qualitative approach]. Revista Interuniversitaria De Formacion Del Profesorado , 24 (3), 25–46. https://dialnet.unirioja.es/servlet/articulo?codigo=3361180

Huisman, J., & Smits, J. (2015). Keeping children in school: Effects of household and context characteristics on school dropout in 363 districts of 30 developing countries. SAGE Open, 5 (4). https://doi.org/10.1177/2158244015609666

Kagitcibasi, C. (2007). Family, Self, and human development across cultures: Theory and applications (2nd ed.). Routledge Academic. https://doi.org/10.4324/9781315205281

Book   Google Scholar  

Keller, H. (2007). Cultures of infancy . Lawrence Erlbaum Associates.

Kim, A. Y., Sinatra, G. M., & Seyranian, V. (2018). Developing a STEM identity among young women: A social identity perspective. Review of Educational Research, 88 (4), 589–625. https://doi.org/10.3102/0034654318779957

Kitayama, S., Duffy, S., & Uchida, Y. (2007). Self as cultural mode of being. In S. Kitayama & D. Cohen (Eds.), Handbook of cultural psychology (pp. 136–174). Guilford Press.

Kuhn, M. H., & McPartland, T. S. (1954). An empirical investigation of self-attitudes. American Sociological Review , 19 (1), 68–76.

Lawson, A. (2014). Learner identities in the context of undergraduates: A case study. Educational Research , 56 (3), 343–356. https://doi.org/10.1080/00131881.2014.934557

Lessard, A., Butler-Kisber, L., Fortin, L., & Marcotte, D. (2014). Analyzing the discourse of dropouts and resilient students. The Journal of Educational Research , 107 (2), 103–110. https://doi.org/10.1080/00220671.2012.753857

Li, S., Xu, Q., & Xia, R. (2020). Relationship between SES and Academic Achievement of Junior High School students in China: The Mediating Effect of Self-Concept. Frontiers in Psychology , 10 (2513), 1–7. https://doi.org/10.3389/fpsyg.2019.02513

Llorent-Bedmar, V., Navarro-Granados, M., & Cobano-Delgado, V. C. (2021). Pedagogical leadership exercised by the principals of disadvantaged schools in Spain. School Leadership & Management , 41 (3), 239–259. https://doi.org/10.1080/13632434.2021.1872526

Longás, J., De Querol, R., Cussó, I., & Riera, J. (2019). Individual, family, school and community: Multidimensional supports for school success in contexts of poverty. Revista De Educación , 386 , 11–36. https://doi.org/10.4438/1988-592X-RE-2019-386-425

Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review , 98 (2), 224–253. https://doi.org/10.1037/0033-295X.98.2.224

Masten, A. S. (2007). Resilience in developing systems: Progress and promise as the fourth wave rises. Development and Psychopathology , 19 (3), 921–930. https://doi.org/10.1017/S0954579407000442

Masten, A. S., Burt, K., & Coatsworth, J. D. (2006). Competence and psychopathology in development. In D. Cicchetti & D. Cohen (Eds.), Developmental psychopathology: Risk disorder and psychopathology (pp. 696–738). Wiley.

Matías-García, J. A. (2022). Identidad de aprendiz y desigualdad social. Resiliencia académica en contextos en riesgo de exclusión social [Learner identity and social inequality. Academic resilience in contexts at risk of social exclusion] . [Unpublished Doctoral Thesis]. Universidad de Sevilla.

Matías-García, J. A., & Cubero-Pérez, R. (2021). La naturaleza heterogénea del concepto de inteligencia en los seres humanos: discursos y contextos de prácticas [The heterogeneous nature of intelligence concept in humans: discourses and contexts of practice]. In Arias-Sánchez, S. (Coord.), Construyendo identidad(es) en escenarios culturales (pp. 97–122). Universidad de Sevilla.

Matias-Garcia, J. A., Cubero, M., & Cubero-Pérez, R. (2024). Laziness and competence: the discursive social construction of resilient learner identity . SSRN. https://doi.org/10.2139/ssrn.4750779

Matías-García, J. A., Santamaría, A., Cubero, M., & Cubero-Pérez, R. (2023). From current to possible selves: Self-descriptions of resilient post-compulsory secondary education Spanish students at risk of social exclusion. Children and Youth Services Review, 155 , 1–10. https://doi.org/10.1016/j.childyouth.2023.107257

McFarland, L., Murray, E., & Phillipson, S. (2016). Student-teacher relationships and student self-concept: Relations with teacher and student gender. Australian Journal of Education , 60 (1), 5–25. https://doi.org/10.1177/0004944115626426

Mena, L., Fernández, M., & Riviére, J. (2010). Desenganchados de la educación: procesos, experiencias, motivaciones y estrategias del abandono y del fracaso escolar [Disengaged from education: Processes, experiences, motivations and strategies of school dropout and failure]. Revista de Educación, Número extraordinario 2010, 119–145.

Moreno, A. (2015). La Empleabilidad De Los jóvenes en España: Explicando El Elevado Desempleo Juvenil Durante La recesión económica [Youth employability in Spain: Explaining high youth unemployment during the economic recession]. Revista Internacional de Investigación en Ciencias Sociales , 11 (1), 2–24. https://doi.org/10.18004/riics.2015.julio.3-20

Moreno, D., Povedano, A., Martínez, B., & Musitu, G. (2012). Emotional and social problems in adolescents from a gender perspective. The Spanish Journal of Psychology , 15 (3), 1013–1023. https://doi.org/10.5209/rev_SJOP.2012.v15.n3.39392

Reay, D. (2018). Working-class educational failure: Theoretical perspectives, discursive concerns, and methodological approaches. In A. Tarabini, & N. Ingram (Eds.), Educational choices, transitions and aspirations in Europe: Systemic, institutional and subjective challenges (pp. 15–31). Routledge.

Chapter   Google Scholar  

Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context . OUP Oxford.

Romero, E., & Hernández, M. (2019). Análisis De las causas endógenas y exógenas del abandono escolar temprano: Una investigación cualitativa [Analysis of endogenous and exogenous causes of early school dropout: A qualitative research]. Educación XX1 , 22 (1), 263–293. https://doi.org/10.5944/educxx1.21351

Rosales, R., & Cubero, R. (2016). El aprendizaje cooperativo como un espacio para la construcción de identidades de aprendiz saludables [Cooperative learning as a space for building healthy learner identities]. Papeles de Trabajo sobre Cultura, Educación y Desarrollo Humano, 12 (3), 30–37. https://dialnet.unirioja.es/servlet/articulo?codigo=5795140

Rouse, H. L., Choi, J. Y., Riser, Q. H., & Beecher, C. C. (2020). Multiple risks, multiple systems, and academic achievement: A nationally representative birth-to-five investigation. Children and Youth Services Review , 108 , 1–12. https://doi.org/10.1016/j.childyouth.2019.104523

Ruiz, E., García, R., & Rebollo, M. . Á. . (2013). Relaciones de género de adolescentes en contextos educativos. Análisis de redes sociales con perspectiva de género [Gender relations of adolescents in educational contexts. Analysis of social networks with a gender perspective]. Profesorado, Revista de Currículum y Formación del Profesorado, 17 (10), 123–140. https://www.ugr.es/~recfpro/rev171ART8.pdf

Sarrasin, J. B., Nenciovici, L., Foisy, L. M. B., Allaire-Duquette, G., Riopel, M., & Masson, S. (2018). Effects of teaching the concept of neuroplasticity to induce a growth mindset on motivation, achievement, and brain activity: A meta-analysis. Trends in Neuroscience and Education , 12 , 22–31. https://doi.org/10.1016/J.TINE.2018.07.003

Santamaría, A., & Cubero, M. (2019). La construcción narrativa del yo en escenarios y prácticas culturales. Una propuesta metodológica y analítica al estudio de la identidad de frontera [The narrative construction of the self in cultural settings and practices. A methodological and analytical proposal to the study of border identity]. In R. Contreras & M. L. Ruiz-Cansino (Eds.), Estudios de frontera. Volumen 1. Aproximaciones metodológicas (pp. 35–75). Fomento Editorial.

Santamaría, A., de la Mata, M. L., Hansen, T. G. B., & Ruiz, L. (2010). Cultural self-construals of Mexican, Spanish, and Danish college students: Beyond independent and interdependent self. Journal of Cross-Cultural Psychology, 41 (3), 471–477. https://doi.org/10.1177/0022022109359694

Save the Children (2019). Todo lo que debes saber de PISA 2018 sobre equidad [Everything you need to know about equity from PISA 2018] . https://www.savethechildren.es/sites/default/files/imce/dossier_pisa2018_espanadatos.pdf

Scribner, S., & Cole, M. (1981). The psychology of literacy . Harvard University Press.

Sedikides, C., & Brewer, M. B. (2001). Individual self, relational self, collective self . Psychology.

Skinner, E., & Zimmer-Gembeck, M. (2011). Perceived control and the development of coping. In S. Folkman (Ed.), The Oxford handbook of stress, health, and coping (pp. 35–59). Oxford University Press.

Toland, J., & Carrigan, D. (2011). Educational psychology and resilience: New concept, new opportunities. School Psychology International , 32 (1), 95–106. https://doi.org/10.1177/0143034310397284

Triandis, H. C. (1989). The self and social behavior in differing cultural contexts. Psychological Review , 96 (3), 506–520. https://doi.org/10.1037/0033-295X.96.3.506

United Nations. (n.d.). Sustainable development goals. https://www.un.org/sustainabledevelopment/

Werstch, M. (1991). Voices of the mind. Sociocultural approach to mediated action . Harvard University Press.

Yuste, S., Santamaría, A., Cubero, M., & de la Mata, M. L. (2021). (Re) constructing Identity after aphasia. A preliminary study about how people with aphasia describe their selves. In C. Glintborg & M. L. de la Mata (Eds.), Identity construction and illness narratives in persons with disabilities (pp. 84–103). Taylor & Francis Group.

Download references

Funding for open access publishing: Universidad de Sevilla/CBUA This research was carried out within the framework of the research project funded by the Spanish Government, Ministry of Science and Innovation, State Research Agency and European Regional Development Fund-ERDF, entitled “Challenges of the self: Identity reconstruction in situations of inequality and social exclusion” (reference: PSI2016–80112–P). It was also funded by the VI-PPITUS (University of Seville).

Author information

Authors and affiliations.

Department of Experimental Psychology, Universidad de Sevilla, Camilo José Cela Street, 41018, Seville, Spain

Jose Antonio Matías-García, Mercedes Cubero, Andrés Santamaría & Miguel Jesús Bascón

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jose Antonio Matías-García .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

José Antonio Matías-García . Department of Experimental Psychology, Universidad de Sevilla, Camilo José Cela Street, 41018 Seville, Spain. E-mail: [email protected]

Current themes of research :

Educational resilience. Learner identity. Conceptions of intelligence. Early school leaving.

Most relevant publications in the field of Psychology of Education :

Matías-García, J. A., Santamaría, A., Cubero, M., Cubero-Pérez, R. (2023). From current to possible selves: Self-descriptions of resilient post-compulsory secondary education Spanish students at risk of social exclusion. Children and Youth Services Review, 155 , 1-10. https://doi.org/10.1016/j.childyouth.2023.107257

Cubero-Pérez, R., Cubero, M., Matías-García, J. A., & Bascón, M. J. (2023). Learner identity in secondary post-compulsory education students from areas in need of social transformation: An example of resilience. European Journal of Psychology of Education, 0 (0), 1-22, https://doi.org/10.1007/s10212-023-00704-6

Matías-García, J. A. (2022). Identidad de aprendiz y desigualdad social. Resiliencia académica en contextos en riesgo de exclusión social [Learner identity and social inequality. Academic resilience in contexts at risk of social exclusion]. [Unpublished Doctoral Thesis]. Universidad de Sevilla.

Matías-García, J. A., & Cubero-Pérez, R. (2021). La naturaleza heterogénea del concepto de inteligencia en los seres humanos: discurso y contextos de práctica [The heterogeneous nature of the concept of intelligence in humans: Discourse and contexts of practice]. In S. Arias-Sánchez (Coord.), Construyendo identidad(es) en escenarios culturales (pp. 97-122). Editorial Universidad de Sevilla.

Matías-García, J. A., & Cubero-Pérez, R. (2021). Heterogeneity in the conceptions of intelligence of university teaching staff. Culture & Psychology, 27 (3), 451–472. https://doi.org/10.1177/1354067X20936926

Matias-Garcia, J. A., & Cubero-Perez, R. (2020). “Einstein worked his socks of”. Conceptions of intelligence in university teaching staff. International Journal of Educational Psychology, 9 (2), 161–194. https://doi.org/10.17583/ijep.2020.4553

Mercedes Cubero . Department of Experimental Psychology, Universidad de Sevilla, Camilo José Cela Street, 41018 Seville, Spain.

Concept formation processes. Teaching practices. Learner identity. Gender and education. Educational discourse.

Cubero, M., Bascón, M. J., & Cubero-Pérez, R. (2020). “My tutor doesn’t say that”: The legitimized voices in dialogic refection on teaching practices. Dialogic Pedagogy, 8 , 26–44. https://doi.org/10.5195/dpj.2020.311

Cubero, M., Santamaría, A., Prados, M. M., & Arias, S. (2019). Concepciones del aprendizaje de estudiantes en proceso de formación como docentes. [Conceptions on learning of students in the process of training as teachers]. Profesorado. Revista de Curriculum y Formación del Profesorado, 23 (3), 453–471.

Cubero-Pérez, R., Cubero, M., & Bascón, M. J. (2019). The reflective practicum in the process of becoming a teacher: The tutor’s discursive support. Education Sciences, 9 (96), 1–21. https://doi.org/10.3390/educsci9020096

Cubero, M., Contreras, R., & Cubero, R. (2016). Cultural origins and schooling infuence on scientific and everyday concepts: The case of border and citizen on the border concepts. Culture & Psychology, 22 (2), 182–205. https://doi.org/10.1177/1354067X15601189

Cubero, M., Santamaría, A., Rebollo, M. A., Cubero, R., García, R., & Vega, L. (2015). Teachers negotiating discourses of gender (in)equality: The case of equal opportunities reform in Andalusia. Gender & Education, 27 (6), 635–653. https://doi.org/10.1080/09540253.2015.1083947

Andrés Santamaría . Department of Experimental Psychology, Universidad de Sevilla, Camilo José Cela Street, 41018 Seville, Spain.

Identity. Narrative. Cultural psychology. Formal schooling. Gender and self.

De la Mata, M.L., Santamaría, A., Trigo, E., Cubero, M., Arias, S., Antaliková, R., Hansen, T.G.B., & Ruiz, M. (2019). The relationship between sociocultural factors and autobiographical memories from childhood: The role of formal schooling. Memory, 27 (1), 103-114. https://doi.org/10.1080/09658211.2018.1515316

De la Mata, M.L., Santamaría, A., y Ruiz, M.L. (2016). Toward the model of independence: The influence of formal schooling experience on earliest autobiographical memories and self-construals: A preliminary study. Journal of Cross-Cultural Psychology, 47 (5), 670–679.   https://doi.org/10.1177/0022022116635745

Cubero, M., Santamaría, A., Rebollo, M.A., Cubero, R., García, R., y Vega, L. (2015). Teachers negotiating discourses of gender (in)equality: The case of equal opportunities reform in Andalusia. Gender & Education, 27 (6), 635-653. https://doi.org/10.1080/09540253.2015.1083947

De la Mata, M.L., Santamaría, A., Hansen, T.G., & Ruiz, L. (2015). Earliest autobiographical memories in college students from three countries: Towards a situated view. Memory Studies, 8 , 151-168. https://doi.org/10.1177/1750698014543966

Santamaría, A., de la Mata, M. L. y Ruiz, M. L. (2012). Escolarización formal, memoria autobiográfica y concepciones culturales del yo [Formal schooling, autobiographical memory and cultural conceptions of the self]. Infancia y Aprendizaje, 35 , 1, 73-86.

Miguel Jesús Bascón . Department of Experimental Psychology, Universidad de Sevilla, Camilo José Cela Street, 41018 Seville, Spain.

Current themes of research : Gender and education. Learner identity. Teaching-learning processes. Educational discourse.

Saavedra. F. J., Bascón, M. J., Prados. M. M., & Sabuco, A. (2013). Indicadores y criterios de calidad de buenas prácticas coeducativas. Una propuesta innovadora. [Indicators and quality criteria of good coeducational practices. An innovative proposal]. Profesorado. Revista de curriculum y formación del profesorado, 17 (1), 201–220.

Rebollo, M. A., Piedra, J., Sala, A., Sabuco, A., Saavedra, F. J., & Bascón, M. J. (2012). La equidad de género en educación: Análisis y descripción de buenas prácticas en educación. [Gender equity in education: Analysis and description of good practices in education]. Revista de Educación, 358 , 129–152.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Matías-García, J., Cubero, M., Santamaría, A. et al. The learner identity of adolescents with trajectories of resilience: the role of risk, academic experience, and gender. Eur J Psychol Educ (2024). https://doi.org/10.1007/s10212-024-00839-0

Download citation

Received : 05 October 2023

Revised : 29 January 2024

Accepted : 11 April 2024

Published : 22 May 2024

DOI : https://doi.org/10.1007/s10212-024-00839-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Learner identity
• Risk of social exclusion
• Early school leaving
• Academic self-concept
• Post-compulsory education
• Find a journal
• Publish with us
• Track your research