difference between case study and descriptive research

Case Study vs. Descriptive Approach to Research

What's the difference.

The case study approach and the descriptive approach are two different methods used in research. The case study approach involves in-depth analysis of a specific individual, group, or situation. It aims to provide a detailed understanding of the subject matter by examining various aspects and collecting qualitative data. On the other hand, the descriptive approach focuses on describing and summarizing a larger population or phenomenon. It involves collecting quantitative data through surveys, observations, or experiments to draw general conclusions. While the case study approach provides rich and detailed information, it is limited in terms of generalizability. In contrast, the descriptive approach allows for broader generalizations but may lack the depth and context provided by case studies. Ultimately, the choice between these approaches depends on the research objectives and the nature of the research question.

Further Detail

Introduction.

Research is a fundamental aspect of any scientific inquiry, aiming to gather information and gain insights into various phenomena. When conducting research, researchers employ different approaches and methodologies to achieve their objectives. Two commonly used approaches are the case study and descriptive approach. While both approaches have their unique attributes, they differ in terms of their focus, data collection methods, and generalizability.

Case Study Approach

The case study approach is a qualitative research method that focuses on in-depth analysis of a specific individual, group, or event. It aims to provide a comprehensive understanding of the subject under investigation by examining its context, history, and unique characteristics. Case studies often involve multiple sources of data, such as interviews, observations, and document analysis, to gather rich and detailed information.

One of the key attributes of the case study approach is its ability to explore complex and unique phenomena that may not be easily captured by other research methods. By delving deep into a specific case, researchers can uncover intricate details and gain a holistic understanding of the subject. This approach is particularly useful when studying rare or exceptional cases, as it allows researchers to examine the intricacies and nuances that may not be apparent in larger-scale studies.

Furthermore, the case study approach enables researchers to generate new hypotheses and theories by closely examining the relationships and patterns within the case. It provides an opportunity for researchers to explore and develop new ideas, which can contribute to the advancement of knowledge in a particular field. Additionally, case studies often involve a longitudinal design, allowing researchers to track changes and developments over time.

However, it is important to note that the case study approach has limitations. Due to its focus on a specific case, the findings may not be easily generalizable to a larger population. The small sample size and unique characteristics of the case may limit the external validity of the findings. Therefore, caution should be exercised when applying the results of a case study to broader contexts.

Descriptive Approach

The descriptive approach, also known as the survey method, aims to describe and analyze the characteristics, behaviors, and opinions of a specific population or sample. It involves collecting data through questionnaires, interviews, or observations, and analyzing the responses to draw conclusions about the population under study. The descriptive approach provides a snapshot of the current state of affairs and allows researchers to identify patterns and trends.

One of the key attributes of the descriptive approach is its ability to provide a broad overview of a population or phenomenon. By collecting data from a large sample, researchers can make generalizations about the population and draw conclusions that are applicable to a wider context. This approach is particularly useful when studying large populations or when the research objective is to describe the prevalence of certain characteristics or behaviors.

Moreover, the descriptive approach allows researchers to quantify data and analyze it statistically. By using statistical techniques, researchers can identify relationships between variables, test hypotheses, and make predictions. This quantitative aspect of the descriptive approach provides a level of objectivity and allows for comparisons across different groups or populations.

However, the descriptive approach also has limitations. It may not capture the complexity and richness of individual cases or unique phenomena. The focus on generalizability may overlook important contextual factors that influence the research topic. Additionally, the reliance on self-report measures in surveys may introduce biases and inaccuracies in the data collected.

While the case study and descriptive approaches differ in their focus and data collection methods, they both contribute to the field of research in their own ways. The case study approach provides in-depth insights into specific cases, allowing researchers to explore complex phenomena and generate new hypotheses. On the other hand, the descriptive approach provides a broader overview of populations, enabling researchers to make generalizations and identify patterns.

Both approaches have their strengths and weaknesses, and the choice between them depends on the research objectives and the nature of the phenomenon under investigation. Researchers should carefully consider the specific research question, the available resources, and the desired level of generalizability when selecting the appropriate approach.

In conclusion, the case study and descriptive approaches are two distinct research methodologies that offer different perspectives and insights. The case study approach allows for in-depth analysis of specific cases, providing rich and detailed information. On the other hand, the descriptive approach provides a broader overview of populations, allowing for generalizations and statistical analysis. Both approaches have their merits and limitations, and researchers should choose the most appropriate approach based on their research objectives and the nature of the phenomenon under investigation.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Module 2: Research and Ethics in Abnormal Psychology

Descriptive research and case studies, learning objectives.

• Explain the importance and uses of descriptive research, especially case studies, in studying abnormal behavior

Types of Research Methods

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. 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 three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists 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 how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While surveys allow results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While existing records can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later, there is a tremendous amount of control over variables of interest. While performing an experiment is a powerful approach, experiments are often conducted in very artificial settings, which calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Clinical or Case Studies

Psychologists can use a detailed description of one person or a small group based on careful observation.  Case studies  are intensive studies of individuals and have commonly been seen as a fruitful way to come up with hypotheses and generate theories. Case studies add descriptive richness. Case studies are also useful for formulating concepts, which are an important aspect of theory construction. Through fine-grained knowledge and description, case studies can fully specify the causal mechanisms in a way that may be harder in a large study.

Sigmund Freud   developed  many theories from case studies (Anna O., Little Hans, Wolf Man, Dora, etc.). F or example, he conducted a case study of a man, nicknamed “Rat Man,”  in which he claimed that this patient had been cured by psychoanalysis.  T he nickname derives from the fact that among the patient’s many compulsions, he had an obsession with nightmarish fantasies about rats. 

Today, more commonly, case studies reflect an up-close, in-depth, and detailed examination of an individual’s course of treatment. Case studies typically include a complete history of the subject’s background and response to treatment. From the particular client’s experience in therapy, the therapist’s goal is to provide information that may help other therapists who treat similar clients.

Case studies are generally a single-case design, but can also be a multiple-case design, where replication instead of sampling is the criterion for inclusion. Like other research methodologies within psychology, the case study must produce valid and reliable results in order to be useful for the development of future research. Distinct advantages and disadvantages are associated with the case study in psychology.

A commonly described limit of case studies is that they do not lend themselves to generalizability . The other issue is that the case study is subject to the bias of the researcher in terms of how the case is written, and that cases are chosen because they are consistent with the researcher’s preconceived notions, resulting in biased research. Another common problem in case study research is that of reconciling conflicting interpretations of the same case history.

Despite these limitations, there are advantages to using case studies. One major advantage of the case study in psychology is the potential for the development of novel hypotheses of the  cause of abnormal behavior   for later testing. Second, the case study can provide detailed descriptions of specific and rare cases and help us study unusual conditions that occur too infrequently to study with large sample sizes. The major disadvantage is that case studies cannot be used to determine causation, as is the case in experimental research, where the factors or variables hypothesized to play a causal role are manipulated or controlled by the researcher. 

Single-Case Experimental Designs

The lack of control available in the traditional case study research strategy led researchers to develop more sophisticated methods, such as single-subject research, which provides the statistical framework for making inferences from quantitative case-study data.

Figure 1 . Antipsychotics are the treatment of choice in managing schizophrenia and other psychotic disorders. Several major trials have been conducted examining the clinical difference between typical antipsychotics and atypical antipsychotics and how the selection may affect the quality of life.

The single-case experimental design  (sometimes called  single-participant research designs ), is particularly useful for studies of treatment effectiveness.  In  single-case experimental designs ,  the same  research participant  serves as the subject in both the experimental and control conditions.  One of the most common forms of the single-case experimental design is the A-B-A-B design, or  reversal design ,  reflecting the alternation between conditions, or phases A and B. The  AB design is a two-part or phase design composed of a baseline (“A” phase) with no changes, and a treatment or intervention (“B”) phase.  If there is a change, then the treatment may be said to have had an effect. However, it is subject to many possible competing hypotheses, making strong conclusions difficult. The A-B-A-B design, or reversal design, is a variant on the AB design. It introduces ways to control for the competing hypotheses and allows for stronger conclusions. T he reversal design (ABAB) is the most powerful of the single-subject research designs because it shows a strong reversal from baseline (“A”) to treatment (“B”) and back again. In an ABAB design, researchers observe behaviors in the “A” phase, institute treatment in the “B” phase, and then repeat the process. If the variable returns to baseline measure without treatment and then resumes its effects when reapplied, the researcher can have greater confidence in the efficacy of that treatment. However, many interventions cannot be reversed for ethical reasons (e.g., involving self-injurious behavior like smoking).  It may be unethical to end an experiment on a baseline measure if the treatment is self-sustaining and highly beneficial and/or related to health. Control condition participants may also deserve the benefits of research once all data has been collected. It is a researcher’s ethical duty to maximize benefits and to ensure that all participants have access to those benefits when possible.

Figure 2. The investigator looks for evidence that the change in the observed behavior occurred coincident with treatment. If the problem behavior declines whenever treatment is introduced (during the first and second treatment phases) but returns (is “reversed”) to baseline levels during the reversal phase, the experimenter can be reasonably confident the treatment had the intended effect.

Link to Learning: Famous Case Studies

Some well-known case studies that related to abnormal psychology include the following:

• Harlow— Phineas Gage
• Breuer & Freud (1895)— Anna O.
• Cleckley’s case studies: on psychopathy ( The Mask of Sanity ) (1941) and multiple personality disorder ( The Three Faces of Eve ) (1957)
• Freud and  Little Hans
• Freud and the  Rat Man
• John Money and the  John/Joan case
• Genie (feral child)
• Piaget’s studies
• Rosenthal’s book on the  murder of Kitty Genovese
• Washoe (sign language)
• Patient H.M.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about handwashing, we have other options available to us.

Suppose we send a researcher to a school playground to observe how aggressive or socially anxious children interact with peers. Will our observer blend into the playground environment by wearing a white lab coat, sitting with a clipboard, and staring at the swings? We want our researcher to be inconspicuous and unobtrusively positioned—perhaps pretending to be a school monitor while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

Figure 3 . In naturalistic observation, psychologists take their research into the streets, homes, restaurants, schools, and other settings where behavior can be directly observed.

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. For example, psychologists have spent weeks observing the behavior of homeless people on the streets, in train stations, and bus terminals. They try to ensure that their naturalistic observations are unobtrusive, so as to minimize interference with the behavior they observe. Nevertheless, the presence of the observer may distort the behavior that is observed, and this must be taken into consideration (Figure 1).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. Although something as simple as observation may seem like it would be a part of all research methods, participant observation is a distinct methodology that involves the researcher embedding themselves into a group in order to study its dynamics. For example, Festinger, Riecken, and Shacter (1956) were very interested in the psychology of a particular cult. However, this cult was very secretive and wouldn’t grant interviews to outside members. So, in order to study these people, Festinger and his colleagues pretended to be cult members, allowing them access to the behavior and psychology of the cult. Despite this example, it should be noted that the people being observed in a participant observation study usually know that the researcher is there to study them. [1]

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

Figure 4 . Surveys can be administered in a number of ways, including electronically administered research, like the survey shown here. (credit: Robert Nyman)

There is both strength and weakness in surveys when compared to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people do not always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the U.S. Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think iT Over

Research has shown that parental depressive symptoms are linked to a number of negative child outcomes. A classmate of yours is interested in  the associations between parental depressive symptoms and actual child behaviors in everyday life [2] because this associations remains largely unknown. After reading this section, what do you think is the best way to better understand such associations? Which method might result in the most valid data?

A-B-A-B design:  an experimental design in which the a person is given treatment, or experimental condition (B), to compare against the baseline (A), and this repeats in order to determine effectiveness

clinical or case study:  observational research study focusing on one or a few people

correlational research:  tests whether a relationship exists between two or more variables

descriptive research:  research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured

experimental research:  tests a hypothesis to determine cause-and-effect relationships

generalizability:  inferring that the results for a sample apply to the larger population

inter-rater reliability:  measure of agreement among observers on how they record and classify a particular event

naturalistic observation:  observation of behavior in its natural setting

observer bias:  when observations may be skewed to align with observer expectations

population:  overall group of individuals that the researchers are interested in

sample:  subset of individuals selected from the larger population

single-case experimental design:   when the same  research participant  serves as the subject in both the experimental and control conditions

survey:  list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

• Scollon, C. N. (2020). Research designs. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http://noba.to/acxb2thy ↵
• Slatcher, R. B., & Trentacosta, C. J. (2011). A naturalistic observation study of the links between parental depressive symptoms and preschoolers' behaviors in everyday life. Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43), 25(3), 444–448. https://doi.org/10.1037/a0023728 ↵
• Modification and adaptation. Authored by : Sonja Ann Miller for Lumen Learning. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike
• Approaches to Research. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:iMyFZJzg@5/Approaches-to-Research . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]
• Descriptive Research. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ . License : CC BY-SA: Attribution-ShareAlike
• Case Study. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Case_study . License : CC BY-SA: Attribution-ShareAlike
• Rat man. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Rat_Man#Legacy . License : CC BY-SA: Attribution-ShareAlike
• Case study in psychology. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Case_study_in_psychology . License : CC BY-SA: Attribution-ShareAlike
• Research Designs. Authored by : Christie Napa Scollon. Provided by : Singapore Management University. Located at : https://nobaproject.com/modules/research-designs#reference-6 . Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Single subject design. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Single-subject_design . License : CC BY-SA: Attribution-ShareAlike
• Single subject research. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Single-subject_research#A-B-A-B . License : Public Domain: No Known Copyright
• Pills. Authored by : qimono. Provided by : Pixabay. Located at : https://pixabay.com/illustrations/pill-capsule-medicine-medical-1884775/ . License : CC0: No Rights Reserved
• ABAB Design. Authored by : Doc. Yu. Provided by : Wikimedia. Located at : https://commons.wikimedia.org/wiki/File:A-B-A-B_Design.png . License : CC BY-SA: Attribution-ShareAlike

Have a language expert improve your writing

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

• Knowledge Base
• Methodology
• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Prevent plagiarism, run a free check.

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 22 April 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

Is this article helpful?

Shona McCombes

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

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

Was this resource helpful?

• << Previous: Thematic Data Analysis in Qualitative Design
• Next: Journal Article Reporting Standards (JARS) >>
• Last Updated: Apr 28, 2024 5:36 AM
• URL: https://resources.nu.edu/researchtools

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

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

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 2.2 “Characteristics of the Three Research Designs” , 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.

Table 2.2 Characteristics of the Three Research Designs

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

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).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

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 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

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 2.5 “Height Distribution” , 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 .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

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 2.5 “Height Distribution” , 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 (see Figure 2.6 “Family Income Distribution” ), 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 2.6 “Family Income Distribution” 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).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

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 2.6 “Family Income Distribution” 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 2.5 “Height Distribution” 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. 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 like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

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 2.10 “Examples of Scatter Plots” , 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 2.10 “Examples of Scatter Plots” , 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 2.10 “Examples of Scatter Plots” 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 2.10 “Examples of Scatter Plots” 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 .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

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

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 2.11 “Prediction of Job Performance From Three Predictor Variables” 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.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

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 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 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 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 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 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 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:

Figure 2.2.3

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 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

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 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?

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.

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

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

• En español – ExME
• Em português – EME

An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

Subscribe to our newsletter

You will receive our monthly newsletter and free access to Trip Premium.

Related Articles

Cluster Randomized Trials: Concepts

This blog summarizes the concepts of cluster randomization, and the logistical and statistical considerations while designing a cluster randomized controlled trial.

Expertise-based Randomized Controlled Trials

This blog summarizes the concepts of Expertise-based randomized controlled trials with a focus on the advantages and challenges associated with this type of study.

A well-designed cohort study can provide powerful results. This blog introduces prospective and retrospective cohort studies, discussing the advantages, disadvantages and use of these type of study designs.

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
• Perspect Clin Res
• v.10(1); Jan-Mar 2019

Study designs: Part 2 – Descriptive studies

Rakesh aggarwal.

Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Priya Ranganathan

1 Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

One of the first steps in planning a research study is the choice of study design. The available study designs are divided broadly into two types – observational and interventional. Of the various observational study designs, the descriptive design is the simplest. It allows the researcher to study and describe the distribution of one or more variables, without regard to any causal or other hypotheses. This article discusses the subtypes of descriptive study design, and their strengths and limitations.

INTRODUCTION

In our previous article in this series,[ 1 ] we introduced the concept of “study designs”– as “the set of methods and procedures used to collect and analyze data on variables specified in a particular research question.” Study designs are primarily of two types – observational and interventional, with the former being loosely divided into “descriptive” and “analytical.” In this article, we discuss the descriptive study designs.

WHAT IS A DESCRIPTIVE STUDY?

A descriptive study is one that is designed to describe the distribution of one or more variables, without regard to any causal or other hypothesis.

TYPES OF DESCRIPTIVE STUDIES

Descriptive studies can be of several types, namely, case reports, case series, cross-sectional studies, and ecological studies. In the first three of these, data are collected on individuals, whereas the last one uses aggregated data for groups.

Case reports and case series

A case report refers to the description of a patient with an unusual disease or with simultaneous occurrence of more than one condition. A case series is similar, except that it is an aggregation of multiple (often only a few) similar cases. Many case reports and case series are anecdotal and of limited value. However, some of these bring to the fore a hitherto unrecognized disease and play an important role in advancing medical science. For instance, HIV/AIDS was first recognized through a case report of disseminated Kaposi's sarcoma in a young homosexual man,[ 2 ] and a case series of such men with Pneumocystis carinii pneumonia.[ 3 ]

In other cases, description of a chance observation may open an entirely new line of investigation. Some examples include: fatal disseminated Bacillus Calmette–Guérin infection in a baby born to a mother taking infliximab for Crohn's disease suggesting that adminstration of infliximab may bring about reactivation of tuberculosis,[ 4 ] progressive multifocal leukoencephalopathy following natalizumab treatment – describing a new adverse effect of drugs that target cell adhesion molecule α4-integrin,[ 5 ] and demonstration of a tumor caused by invasive transformed cancer cells from a colonizing tapeworm in an HIV-infected person.[ 6 ]

Cross-sectional studies

Studies with a cross-sectional study design involve the collection of information on the presence or level of one or more variables of interest (health-related characteristic), whether exposure (e.g., a risk factor) or outcome (e.g., a disease) as they exist in a defined population at one particular time. If these data are analyzed only to determine the distribution of one or more variables, these are “descriptive.” However, often, in a cross-sectional study, the investigator also assesses the relationship between the presence of an exposure and that of an outcome. Such cross-sectional studies are referred to as “analytical” and will be discussed in the next article in this series.

Cross-sectional studies can be thought of as providing a “snapshot” of the frequency and characteristics of a disease in a population at a particular point in time. These are very good for measuring the prevalence of a disease or of a risk factor in a population. Thus, these are very helpful in assessing the disease burden and healthcare needs.

Let us look at a study that was aimed to assess the prevalence of myopia among Indian children.[ 7 ] In this study, trained health workers visited schools in Delhi and tested visual acuity in all children studying in classes 1–9. Of the 9884 children screened, 1297 (13.1%) had myopia (defined as spherical refractive error of −0.50 diopters (D) or worse in either or both eyes), and the mean myopic error was −1.86 ± 1.4 D. Furthermore, overall, 322 (3.3%), 247 (2.5%) and 3 children had mild, moderate, and severe visual impairment, respectively. These parts of the study looked at the prevalence and degree of myopia or of visual impairment, and did not assess the relationship of one variable with another or test a causative hypothesis – these qualify as a descriptive cross-sectional study. These data would be helpful to a health planner to assess the need for a school eye health program, and to know the proportion of children in her jurisdiction who would need corrective glasses.

The authors did, subsequently in the paper, look at the relationship of myopia (an outcome) with children's age, gender, socioeconomic status, type of school, mother's education, etc. (each of which qualifies as an exposure). Those parts of the paper look at the relationship between different variables and thus qualify as having “analytical” cross-sectional design.

Sometimes, cross-sectional studies are repeated after a time interval in the same population (using the same subjects as were included in the initial study, or a fresh sample) to identify temporal trends in the occurrence of one or more variables, and to determine the incidence of a disease (i.e., number of new cases) or its natural history. Indeed, the investigators in the myopia study above visited the same children and reassessed them a year later. This separate follow-up study[ 8 ] showed that “new” myopia had developed in 3.4% of children (incidence rate), with a mean change of −1.09 ± 0.55 D. Among those with myopia at the time of the initial survey, 49.2% showed progression of myopia with a mean change of −0.27 ± 0.42 D.

Cross-sectional studies are usually simple to do and inexpensive. Furthermore, these usually do not pose much of a challenge from an ethics viewpoint.

However, this design does carry a risk of bias, i.e., the results of the study may not represent the true situation in the population. This could arise from either selection bias or measurement bias. The former relates to differences between the population and the sample studied. The myopia study included only those children who attended school, and the prevalence of myopia could have been different in those did not attend school (e.g., those with severe myopia may not be able to see the blackboard and hence may have been more likely to drop out of school). The measurement bias in this study would relate to the accuracy of measurement and the cutoff used. If the investigators had used a cutoff of −0.25 D (instead of −0.50 D) to define myopia, the prevalence would have been higher. Furthermore, if the measurements were not done accurately, some cases with myopia could have been missed, or vice versa, affecting the study results.

Ecological studies

Ecological (also sometimes called as correlational) study design involves looking for association between an exposure and an outcome across populations rather than in individuals. For instance, a study in the United States found a relation between household firearm ownership in various states and the firearm death rates during the period 2007–2010.[ 9 ] Thus, in this study, the unit of assessment was a state and not an individual.

These studies are convenient to do since the data have often already been collected and are available from a reliable source. This design is particularly useful when the differences in exposure between individuals within a group are much smaller than the differences in exposure between groups. For instance, the intake of particular food items is likely to vary less between people in a particular group but can vary widely across groups, for example, people living in different countries.

However, the ecological study design has some important limitations.First, an association between exposure and outcome at the group level may not be true at the individual level (a phenomenon also referred to as “ecological fallacy”).[ 10 ] Second, the association may be related to a third factor which in turn is related to both the exposure and the outcome, the so-called “confounding”. For instance, an ecological association between higher income level and greater cardiovascular mortality across countries may be related to a higher prevalence of obesity. Third, migration of people between regions with different exposure levels may also introduce an error. A fourth consideration may be the use of differing definitions for exposure, outcome or both in different populations.

Descriptive studies, irrespective of the subtype, are often very easy to conduct. For case reports, case series, and ecological studies, the data are already available. For cross-sectional studies, these can be easily collected (usually in one encounter). Thus, these study designs are often inexpensive, quick and do not need too much effort. Furthermore, these studies often do not face serious ethics scrutiny, except if the information sought to be collected is of confidential nature (e.g., sexual practices, substance use, etc.).

Descriptive studies are useful for estimating the burden of disease (e.g., prevalence or incidence) in a population. This information is useful for resource planning. For instance, information on prevalence of cataract in a city may help the government decide on the appropriate number of ophthalmologic facilities. Data from descriptive studies done in different populations or done at different times in the same population may help identify geographic variation and temporal change in the frequency of disease. This may help generate hypotheses regarding the cause of the disease, which can then be verified using another, more complex design.

DISADVANTAGES

As with other study designs, descriptive studies have their own pitfalls. Case reports and case-series refer to a solitary patient or to only a few cases, who may represent a chance occurrence. Hence, conclusions based on these run the risk of being non-representative, and hence unreliable. In cross-sectional studies, the validity of results is highly dependent on whether the study sample is well representative of the population proposed to be studied, and whether all the individual measurements were made using an accurate and identical tool, or not. If the information on a variable cannot be obtained accurately, for instance in a study where the participants are asked about socially unacceptable (e.g., promiscuity) or illegal (e.g., substance use) behavior, the results are unlikely to be reliable.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

• 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

The theory contribution of case study research designs

• Original Research
• Open access
• Published: 16 February 2017
• Volume 10 , pages 281–305, ( 2017 )

Cite this article

You have full access to this open access article

• Hans-Gerd Ridder 1  

162k Accesses

296 Citations

11 Altmetric

Explore all metrics

The objective of this paper is to highlight similarities and differences across various case study designs and to analyze their respective contributions to theory. Although different designs reveal some common underlying characteristics, a comparison of such case study research designs demonstrates that case study research incorporates different scientific goals and collection and analysis of data. This paper relates this comparison to a more general debate of how different research designs contribute to a theory continuum. The fine-grained analysis demonstrates that case study designs fit differently to the pathway of the theory continuum. The resulting contribution is a portfolio of case study research designs. This portfolio demonstrates the heterogeneous contributions of case study designs. Based on this portfolio, theoretical contributions of case study designs can be better evaluated in terms of understanding, theory-building, theory development, and theory testing.

Similar content being viewed by others

What is Qualitative in Qualitative Research

Reporting reliability, convergent and discriminant validity with structural equation modeling: a review and best-practice recommendations.

Criteria for Good Qualitative Research: A Comprehensive Review

Avoid common mistakes on your manuscript.

1 Introduction

Case study research scientifically investigates into a real-life phenomenon in-depth and within its environmental context. Such a case can be an individual, a group, an organization, an event, a problem, or an anomaly (Burawoy 2009 ; Stake 2005 ; Yin 2014 ). Unlike in experiments, the contextual conditions are not delineated and/or controlled, but part of the investigation. Typical for case study research is non-random sampling; there is no sample that represents a larger population. Contrary to quantitative logic, the case is chosen, because the case is of interest (Stake 2005 ), or it is chosen for theoretical reasons (Eisenhardt and Graebner 2007 ). For within-case and across-case analyses, the emphasis in data collection is on interviews, archives, and (participant) observation (Flick 2009 : 257; Mason 2002 : 84). Case study researchers usually triangulate data as part of their data collection strategy, resulting in a detailed case description (Burns 2000 ; Dooley 2002 ; Eisenhardt 1989 ; Ridder 2016 ; Stake 2005 : 454). Potential advantages of a single case study are seen in the detailed description and analysis to gain a better understanding of “how” and “why” things happen. In single case study research, the opportunity to open a black box arises by looking at deeper causes of the phenomenon (Fiss 2009 ). The case data can lead to the identification of patterns and relationships, creating, extending, or testing a theory (Gomm et al. 2000 ). Potential advantages of multiple case study research are seen in cross-case analysis. A systematic comparison in cross-case analysis reveals similarities and differences and how they affect findings. Each case is analyzed as a single case on its own to compare the mechanisms identified, leading to theoretical conclusions (Vaughan 1992 : 178). As a result, case study research has different objectives in terms of contributing to theory. On the one hand, case study research has its strength in creating theory by expanding constructs and relationships within distinct settings (e.g., in single case studies). On the other hand, case study research is a means of advancing theories by comparing similarities and differences among cases (e.g., in multiple case studies).

Unfortunately, such diverging objectives are often neglected in case study research. Burns ( 2000 : 459) emphasizes: “The case study has unfortunately been used as a ‘catch –all’ category for anything that does not fit into experimental, survey, or historical methods.”

Therefore, this paper compares case study research designs. Such comparisons have been conducted previously regarding their philosophical assumptions and orientations, key elements of case study research, their range of application, and the lacks of methodological procedures in publications. (Baxter and Jack 2008 ; Dooley 2002 ; Dyer and Wilkins 1991 ; Piekkari et al. 2009 ; Welch et al. 2011 ). This paper aims to compare case study research designs regarding their contributions to theory.

Case study research designs will be analyzed regarding their various strengths on a theory continuum. Edmondson and McManus ( 2007 ) initiated a debate on whether the stage of theory fits to research questions, style of data collection, and analyses. Similarly, Colquitt and Zapata-Phelan ( 2007 ) created a taxonomy capturing facets of empirical article’s theoretical contributions by distinguishing between theory-building and theory testing. Corley and Gioia ( 2011 ) extended this debate by focusing on the practicality of theory and the importance of prescience. While these papers consider the whole range of methodological approaches on a higher level, they treat case studies as relatively homogeneous. This paper aims to delve into a deeper level of analysis by solely focusing on case study research designs and their respective fit on this theory continuum. This approach offers a more fine-grained understanding that sheds light on the diversity of case study research designs in terms of their differential theory contributions. Such a deep level of analysis on case study research designs enables more rigor in theory contribution. To analyze alternative case study research designs regarding their contributions to theory, I engage into the following steps:

First, differences between case study research designs are depicted. I outline and compare the case study research designs with regard to the key elements, esp. differences in research questions, frameworks, sampling, data collection, and data analysis. These differences result in a portfolio of various case study research designs.

Second, I outline and substantiate a theory continuum that varies between theory-building, theory development, and testing theory. Based on this continuum, I analyze and discuss each of the case study research designs with regard to their location on the theory continuum. This analysis is based on a detailed differentiation of the phenomenon (inside or outside the theory), the status of the theory, research strategy, and methods.

As a result, the contribution to the literature is a portfolio of case study research designs explicating their unique contributions to theory. The contribution of this paper lies in a fine-grained analysis of the interplay of methods and theory (van Maanen et al. 2007 ) and the methodological fit (Edmondson and McManus 2007 ) of case study designs and the continuum of theory. It demonstrates that different designs have various strengths and that there is a fit between case study designs and different points on a theory continuum. If there is no clarity as to whether a case study design aims at creating, elaborating, extending, or testing theory, the contribution to theory is difficult to identify for authors, reviewers, and readers. Consequently, this paper aims to clarify at which point of the continuum of theory case study research designs can provide distinct contributions that can be identified beyond their traditionally claimed exploratory character.

2 Differences across case study design: a portfolio approach

Only few papers have compared case study research designs so far. In all of these comparisons, the number of designs differs as well as the issues under consideration. In an early debate between Dyer and Wilkins ( 1991 ) and Eisenhardt ( 1991 ), Dyer and Wilkins compared the case study research design by Eisenhardt ( 1989 ) with “classical” case studies. The core of the debate concerns a difference between in-depth single case studies (classical case study) to a focus on the comparison of multiple cases. Dyer and Wilkins ( 1991 : 614) claim that the essence of a case study lies in the careful study of a single case to identify new relationships and, as a result, question the Eisenhardt approach which puts a lot of emphasis on comparison of multiple cases. Eisenhardt, on the contrary, claims that multiple cases allow replication between cases and is, therefore, seen as a means of corroboration of propositions (Eisenhardt 1991 ). Classical case studies prefer deep descriptions of a single case, considering the context to reveal insights into the single case and by that elaborate new theories. The comparison of multiple cases, therefore, tends—in the opinion of Dyer and Wilkens—to surface descriptions. This weakens the possibility of context-related, rich descriptions. While, in classic case study, good stories are the aim, the development of good constructs and their relationships is aimed in Eisenhardt’s approach. Eisenhardt ( 1991 : 627) makes a strong plea on more methodological rigor in case study research, while Dyer and Wilkins ( 1991 : 613) criticize that the new approach “… includes many of the attributes of hypothesis-testing research (e.g., sampling and controls).”

Dooley ( 2002 : 346) briefly takes the case study research designs by Yin (1994) and Eisenhardt ( 1989 ) as exemplars of how the processes of case study research can be applied. The approach by Eisenhardt is seen as an exemplar that advances conceptualization and operationalization in the phases of theory-building, while the approach by Yin is seen as exemplar that advances minimally conceptualized and operationalized existing theory.

Baxter and Jack ( 2008 ) describe the designs by Yin (2003) and Stake ( 1995 ) to demonstrate key elements of qualitative case study. The authors outline and carefully compare the approaches by Yin and Stake in conducting the research process, neglecting philosophical differences and theoretical goals.

Piekkari et al. ( 2009 ) outline the methodological richness of case study research using the approaches of Yin et al. (1998), and Stake. They specifically exhibit the role of philosophical assumptions, establishing differences in conventionally accepted practices of case study research in published papers. The authors analyze 135 published case studies in four international business journals. The analysis reveals that, in contrast to the richness of case study approaches, the majority of published case studies draw on positivistic foundations and are narrowly declared as explorative with a lack of clarity of the theoretical purpose of the case study. Case studies are often designed as multiple case studies with cross-sectional designs based on interviews. In addition to the narrow use of case study research, the authors find out that “… most commonly cited methodological literature is not consistently followed” (Piekkari et al. 2009 : 567).

Welch et al. ( 2011 ) develop a typology of theorizing modes in case study methods. Based on the two dimensions “contextualization” and “causal explanation”, they differentiate in their typology between inductive theory-building (Eisenhardt), interpretive sensemaking (Stake), natural experiment (Yin), and contextualised explanation (Ragin/Bhaskar). The typology is used to analyze 199 case studies from three highly ranked journals over a 10-year period for whether the theorizing modes are exercised in the practice of publishing case studies. As a result, the authors identify a strong emphasis on the exploratory function of case studies, neglecting the richness of case study methods to challenge, refine, verify, and test theories (Welch et al. 2011 : 755). In addition, case study methods are not consistently related to theory contribution: “By scrutinising the linguistic elements of texts, we found that case researchers were not always clear and consistent in the way that they wrote up their theorising purpose and process” (Welch et al. 2011 : 756).

As a result, the comparisons reveal a range of case study designs which are rarely discussed. In contrast, published case studies are mainly introduced as exploratory design. Explanatory, interpretivist, and critical/reflexive designs are widely neglected, narrowing the possible applications of case study research. In addition, comparisons containing an analysis of published case studies reveal a low degree in accuracy when applying case study methods.

What is missing is a comparison of case study research designs with regard to differences in the contribution to theory. Case study designs have different purposes in theory contribution. Confusing these potential contributions by inconsistently utilizing the appropriate methods weakens the contribution of case studies to scientific progress and, by that, damages the reputation of case studies.

To conduct such a comparison, I consider the four case study research approaches of Yin, Eisenhardt, Burawoy, and Stake for the following reasons.

These approaches are the main representatives of case study research design outlined in the comparisons elaborated above (Baxter and Jack 2008 ; Dooley 2002 ; Dyer and Wilkins 1991 ; Piekkari et al. 2009 ; Welch et al. 2011 ). I follow especially the argument by Piekkari et al. ( 2009 ) that these approaches contain a broad spectrum of methodological foundations of exploratory, explanatory, interpretivist, and critical/reflexive designs. The chosen approaches have an explicit and detailed methodology which can be reconstructed and compared with regard to their theory contribution. Although there are variations in the application of the designs, to the best of my knowledge, the designs represent the spectrum of case study methodologies. A comparison of these methodologies revealed main distinguishable differences. To highlight these main differences, I summarized these differences into labels of “no theory first”; “gaps and holes”; “social construction of reality”; and “anomalies”.

I did not consider descriptions of case study research in text books which focus more or less on general descriptions of the common characteristics of case studies, but do not emphasize differences in methodologies and theory contribution. In addition, I did not consider so-called “home grown” designs (Eisenhardt 1989 : 534) which lack a systematic and explicit demonstration of the methodology and where “… the hermeneutic process of inference—how all these interviews, archival records, and notes were assembled into a coherent whole, what was counted and what was discounted—remains usually hidden from the reader” (Fiss 2009 : 425).

Finally, although often cited in the methodological section of case studies, books are not considered which concentrate on data analysis in qualitative research per se (Miles et al. 2014 ; Corbin and Strauss 2015 ). Therefore, to analyze the contribution of case study research to the scientific development, it needs to compare explicit methodology. This comparison will be outlined in the following sections with regard to main methodological steps: the role of the case, the collection of data, and the analysis of data.

2.1 Case study research design 1: no theory first

A popular template for building theory from case studies is a paper by Eisenhardt ( 1989 ). It follows a dramaturgy with a precise order of single steps for constructing a case study and is one of the most cited papers in methods sections (Ravenswood 2011 ). This is impressive for two reasons. On the one hand, Eisenhardt herself has provided a broader spectrum of case study research designs in her own empirical papers, for example, by combining theory-building and theory elaboration (Bingham and Eisenhardt 2011 ). On the other hand, she “updated” her design in a paper with Graebner (Eisenhardt and Graebner 2007 ), particularly by extending the range of inductive theory-building. These developments do not seem to be seriously considered by most authors, as differences and elaborations of this spectrum are rarely found in publications. Therefore, in the following, I focus on the standards provided by Eisenhardt ( 1989 ) and Eisenhardt and Graebner ( 2007 ) as exemplary guidelines.

Eisenhardt follows the ideal of ‘no theory first’ to capture the richness of observations without being limited by a theory. The research question may stem from a research gap meaning that the research question is of relevance. Tentative a priori constructs or variables guide the investigation, but no relationships between such constructs or variables are assumed so far: “Thus, investigators should formulate a research problem and possibly specify some potentially important variables, with some reference to extant literature. However, they should avoid thinking about specific relationships between variables and theories as much as possible, especially at the outset of the process” (Eisenhardt 1989 : 536).

Cases are chosen for theoretical reasons: for the likelihood that the cases offer insights into the phenomenon of interest. Theoretical sampling is deemed appropriate for illuminating and extending constructs and identifying relationships for the phenomenon under investigation (Eisenhardt and Graebner 2007 ). Cases are sampled if they provide an unusual phenomenon, replicate findings from other cases, use contrary replication, and eliminate alternative explanations.

With respect to data collection, qualitative data are the primary choice. Data collection is based on triangulation, where interviews, documents, and observations are often combined. A combination of qualitative data and quantitative data is possible as well (Eisenhardt 1989 : 538). Data analysis is conducted via the search for within-case patterns and cross-case patterns. Systematic procedures are conducted to compare the emerging constructs and relationships with the data, eventually leading to new theory.

A good exemplar for this design is the investigation of technology collaborations (Davis and Eisenhardt 2011 ). The purpose of this paper is to understand processes by which technology collaborations support innovations. Eight technology collaborations among ten firms were sampled for theoretical reasons. Qualitative and quantitative data were used from semi-structured interviews, public and private data, materials provided by informants, corporate intranets, and business publications. The data was measured, coded, and triangulated. Writing case histories was a basis for within-case and cross-case analysis. Iteration between cases and emerging theory and considering the relevant literature provided the basis for the development of a theoretical framework.

Another example is the investigation of what is learned in organizational processes (Bingham and Eisenhardt 2011 ). This paper demonstrates that the case study design is not only used for theory-building, but can also be combined with theory elaboration. Based on the lenses of the organizational knowledge literature, organizational routines literature, and heuristics literature, six technology-based ventures were chosen for theoretical reasons. Several data sources were used, especially quantitative and qualitative data from semi-structured interviews, archival data, observations, e-mails, phone calls, and follow-up interviews. Within-case analysis revealed what each firm has learned from process experience. Cross-case analysis revealed emerging patterns from which tentative constructs and propositions were formed. In replication logic constructs and propositions were refined across the cases. When mirroring the findings with the literature, both the emergences of the constructs were compared and unexpected types were considered. The iteration of theory and data as well as the consideration of related research sharpened the theoretical arguments, eventually leading to a theoretical framework. “Thus, we combined theory elaboration (Lee 1999 ) and theory generation (Eisenhardt 1989 )” (Bingham and Eisenhardt 2011 : 1448).

2.2 Case study research design 2: gaps and holes

Contrary to “No Theory First”, case study research design can also aim at specifying gaps or holes in existing theory with the ultimate goal of advancing theoretical explanations (Ridder 2016 ). A well-known template for this case study research design is the book by Yin ( 2014 ). It is a method-orientated handbook of how to design single and multiple case studies with regard to this purpose. Such a case study research design includes: “A ‘how’ and ‘why’ question” (Yin 2014 : 14). Research questions can be identified and shaped using literature to narrow the interest in a specific topic, looking for key studies and identifying questions in these studies. According to Yin’s design, existing theory is the starting point of case study research. In addition, propositions or frameworks provide direction, reflect the theoretical perspective, and guide the search for relevant evidence.

There are different rationales for choosing a single case design (Yin 2014 : 51). Purposeful sampling is conducted if an extreme case or an unusual case is chosen and if rarely observable phenomena can be investigated with regard to unknown matters and their relationships. Common cases allow conclusions for a broader class of cases. Revelatory cases provide the opportunity to investigate into a previously inaccessible inquiry, and the longitudinal study enables one to investigate a single case at several points in time. A rationale for multiple case designs has its strength in replication logic (Yin 2014 : 56). In the case of literal replication, cases are selected to predict similar results. In the case of theoretical replication, cases are selected to predict contrasting results but for theoretical reasons. Yin provides several tactics to increase the reliability (protocol; data base) of the study.

Yin ( 2014 : 103) emphasizes that interviews are one of the most important sources of data collection but considers other sources of qualitative data as well. Data triangulation is designed to narrow problems of construct validity, as multiple sources of data provide multiple measures of the same phenomenon. Yin ( 2014 : 133) offers a number of data analysis strategies (e.g., case description; examining rival explanations) and analytic techniques which are apt to compare the proposed relationships with empirical patterns. Pattern-matching logic compares empirically based patterns with predicted patterns, enabling further data analysis techniques (explanation building, time series analysis, logic models, and cross-case synthesis). In analytical generalization, the theory is compared with the empirical results, leading to the modification or extension of the theory.

An appropriate model for this case study design can be identified in a paper by Ellonen et al. ( 2009 ). The paper is based on the emerging dynamic capability theory. The four cases were chosen for theoretical reasons to deliver an empirical contribution to the dynamic capability theory by investigating the relationship of dynamic capabilities and innovation outcomes. The authors followed a literal replication strategy and identified patterns between dynamic capabilities of the firms and their innovation outcomes.

Shane ( 2000 ) is an author who developed specific propositions from a framework and examined the propositions in eight entrepreneurial cases. Using several sources of interviews and archival data, the author compared the data with the propositions using the pattern-matching logic, which concluded in developing entrepreneurship theory.

2.3 Case study research design 3: social construction of reality

So far, the outlined case study research designs are based on positivist roots, but there is richness and variety in case study research stemming from different philosophical realms. The case study research design by Stake ( 1995 , 2000 , 2005 ), for example, is based on constructivist assumptions and aims to investigate the social construction of reality and meaning (Schwandt 1994 : 125).

According to this philosophical assumption, there is no unique “real world” that preexists independently of human mental activity and symbolic language. The world is a product of socially and historically related interchanges amongst people (social construction). The access to reality is given through social constructions, such as language and shared meanings: “The meaning-making activities themselves are of central interest to social constructionists/constructivists, simply because it is the meaning-making/sense making attributional activities that shape action or (inaction)” (Guba and Lincoln 2005 : 197). Therefore, the researcher is not looking for objective “facts”, nor does he aim at identifying and measuring patterns which can be generalized. Contrarily, the constructivist is researching into specific actions, in specific places, at specific times. The scientist tries to understand the construction and the sharing of meaning (Schwandt 1994 ).

According to Stake ( 2005 ), the direction of the case study is shaped by the interest in the case. In an intrinsic case study, the case itself is of interest. The purpose is not theory-building but curiosity in the case itself. In an instrumental case study, the case itself is of secondary interest. It plays a supportive role, as it facilitates the understanding of a research issue. The case can be typical of other cases. Multiple or collective case study research designs extend the instrumental case study. It is assumed that a number of cases will increase the understanding and support theorizing by comparison of the cases.

The differentiation by Stake ( 1995 , 2005 ) into intrinsic and instrumental cases guides the purposive sampling strategy. In intrinsic case studies, the case is, by definition, already selected. The researcher looks for specific characteristics, aiming for thick descriptions with the opportunity to learn. Representativeness or generalization is not considered. In instrumental case study design, purposive sampling leads to the phenomenon under investigation. In multiple case study designs, the ability to compare cases enhances the opportunity to theorize.

A case study requires an integrated, holistic comprehension of the case complexity. According to Stake ( 2005 ), the case study is constructed by qualitative data, such as observations, interviews, and documents. Triangulation first serves as clarification of meaning. Second, the researcher is interested in the diversity of perceptions.

Two methods of data analysis are considered in such qualitative case study design: direct interpretation and categorical aggregation (Stake 1995 : 74). The primary task of an intrinsic case study is to understand the case. This interpretation is offered to the reader, but the researcher has to provide the material in a sufficient way (thick descriptions), so that the reader can learn from the case as well as draw his or her own conclusions. Readers can thus make some generalizations based on personal and vicarious experiences (“naturalistic generalization”). In instrumental case studies, the understanding of phenomena and relationships leads to categorical aggregation, and the focus is on how the phenomenon exists across several cases.

Greenwood and Suddaby ( 2006 ), for example, used the instrumental case study design by Stake, combining network location theory and dialectical theory. They identified new dynamics creating a process model of elite institutional entrepreneurship.

Ituma et al. ( 2011 ) highlighted the social construction of reality in their study of career success. The majority of career studies have been conducted in Western countries and findings have been acknowledged as universally applicable. The authors demonstrated that realities of managers in other areas are constructed differently. As a result of their study, they provided a contextually sensitive frame for the analysis of career outcomes.

2.4 Case study research design 4: anomalies

Identifying anomalies as a basis for further research is common in management and organization research (Gilbert and Christensen 2005 ). In case study research, the extended case study method is used for this case study research design (Ridder 2016 ). Following Burawoy ( 1991 , 1998 , 2009 ), the research question derives from curiosity. Researchers normally look at what is “interesting” and what is “surprising” in a social situation that existing theory cannot explain. Initially, it is not important whether the expectations develop from some popular belief, stereotype, or from an academic theory. The extended case study research design is guided by anomalies that the previous theory was not able to explain through internal contradictions of theory, theoretical gaps, or silences. An anomaly does not reject theory, but rather demonstrates that the theory is incomplete. Theory is aimed to be improved by “… turning anomalies into exemplars” (Burawoy 1991 : 10).

The theoretical sampling strategy in this case study research design stems from the theoretical failure in confrontation with the site. According to the reflexive design, such cases do not favour individuals or isolated phenomena, but social situations in which a comparative strategy allows the tracing of differences across the cases to external forces.

In the extended case study, the researcher deals with qualitative data, but also considers the broader complex social situation. The researcher engages into a dialogue with the respondents (Burawoy ( 1991 , 1998 , 2009 ). An interview is an intervention into the life of a respondent. By means of mutual interaction it is possible to discover the social order under investigation. The observer has to unpack those situational experiences by means of participant observation and mutual interpretation. This situational comprehension aims at understanding divergent “voices”, reflecting the variety of respondents’ understandings of the social situation.

As in other sciences, these voices have to be aggregated. This aggregation of multiple readings of a single case is conducted by turning the aggregation into social processes: “The move from situation to process is accomplished differently in different reflexive methods, but it is always reliant on existing theory” (Burawoy 2009 : 41). Social processes are now traced to the external field as the conditions of the social processes. Consequently, this leads to the question concerning “… how those micro situations are shaped by wider structures” (Burawoy 1991 : 282). “Reflexive science insists, therefore, on studying the everyday world from the standpoint of its structuration, that is, by regarding it as simultaneously shaped by and shaping an external field of forces” (Burawoy 2009 : 42). Such social fields cannot be held constant, which undermines the idea of replication. The external field is in continuous flux. Accordingly, social forces that influence the social processes are identified, shaping the phenomenon under investigation. Extension of theory does not target representativeness as a relationship of sample and population. Generality in reflexive science is to reconstruct an existing theory: “We begin with our favorite theory but seek not confirmations but refutations that inspire us to deepen that theory. Instead of discovering grounded theory, we elaborate existing theory. We do not worry about the uniqueness of our case, since we are not as interested in its representativeness as its contribution to reconstructing theory. Our theoretical point of departure can range from the folk theory of participants to any abstract law. We consider only that the scientist consider it worth developing” (Burawoy 2009 : 43). Such elaboration stems from the identification of anomalies and offers new predictions with regard to the theory.

It is somewhat surprising that the extended case study design has been neglected in the management literature so far, and it appears that critical reflexive principles have to be resurrected as they have been in other disciplines (see the overview at Wadham and Warren 2014 ). Examples in the management and organization literature are rare. Danneels ( 2011 ) used the extended case study design to extend the dynamic capabilities theory. In his famous Smith Corona case, Danneels shows how a company tried to change its resource base. Based on detailed data, the Smith Corona case provides insights into the resource alteration processes and how dynamic capabilities operate. As a result, the paper fills a process gap in dynamic capability theory. Iterating between data collection and analysis, Danneels revealed resource cognition as an element not considered so far in dynamic capability theory. The use of the extended case study method is limited to the iteration of data and theory. First, there is “running exchange” (Burawoy 1991 : 10) between field notes and analysis. Second, there is iteration between analysis and existing theory. Unlike Burawoy, who aims to reconstruct existing theory on the basis of “emergent anomalies” (Burawoy 1991 : 11) considering social processes and external forces, Danneels confronts the dynamic capabilities literature with the Smith Corona case to extend the theory of dynamic capabilities.

2.5 A comparison of case study research processes

Commonalities and differences emerged from the comparison of the designs. Table  1 provides a brief summary of these main differences and the resulting portfolio of case study research designs which will be discussed in more detail.

There is an extensive range between the different designs regarding the research processes. In “no theory first”, there is a broad and tentative research question with some preliminary variables at the outset. The research question may be modified during the study as well as the variables. This design avoids any propositions regarding relationships.

On the contrary, the research question in “gaps and holes” is strongly related to existing theory, focusing on “how and why” questions. The existing theory contains research gaps which, once identified within the existing theory, lead accordingly to assumed relationships which are the basis for framework and propositions to be matched by empirical data. This broad difference is even more elaborated by a design that aims the “social construction of reality”. There is no research question at the outset, but a curiosity in the case or the case is a facilitator to understand a research issue. This is far away from curiosity in the “anomaly approach”. Here, the research question is inspired by questioning why an anomaly cannot be explained by the existing theory. What kind of gaps, silences, or internal contradictions demonstrates the insufficiency of the existing theory?

Various sampling strategies are used across these case study research designs, including theoretical sampling and purposeful sampling, which serve different objectives. Theoretical sampling in “no theory first” aims at selecting a case or cases that are appropriate to highlight new or extend preliminary constructs and reveal new relationships. There is a distinct difference from theoretical sampling in the “anomalies” approach. Such a sampling strategy aims to choose a case that is a demonstration of the failure of the theory. In “gaps and holes” sampling is highly focused on the purpose of the case study. Extreme and unusual cases have other purposes compared to common cases or revelatory cases. A single case may be chosen to investigate deeply into new phenomena. A multiple case study may serve a replication logic by which the findings have relevance beyond the cases under investigation. In “social construction of reality”, the sampling is purposeful as well, but for different reasons. Either the case is of interest per se or the case represents a good opportunity to understand a theoretical issue.

Although qualitative data are preferred in all of the designs, quantitative data are seen as a possible opportunity to strengthen cases by such data. Nevertheless, in “social construction of reality”, there is a strong emphasis on thick descriptions and a holistic understanding of the case. This is in contrast to a more construct- and variable- oriented collection of data in “no theory first” and “gaps and holes”. In addition, in contrast to that, the “anomaly” approach is the only design that receives data from dialogue between observer and participants and participant observation.

Finally, data analysis lies within a wide range. In “no theory first”, the research process is finalized by inspecting the emerging constructs within the case or across cases. Based on a priory constructs, systematic comparisons reveal patterns and relationships resulting in a tentative theory. On the contrary, in “gaps and holes”, a tentative theory exists. The final analysis concentrates on the matching of the framework or propositions with patterns from the data. While both of these approaches condense data, the approach of “social construction of reality” ends the research process with thick descriptions of the case to learn from the case or with categorical comparisons. In the “anomaly” approach, the data analysis is aggregation of data, but these aggregated data are related to its external field and their pressures and influences by structuration to reconstruct the theory.

As a result, it is unlikely that the specified case study designs contribute to theory in a homogeneous manner. This result will be discussed in light of the question regarding how these case study designs can inform theory at several points of a continuum of theory. This analysis will be outlined in the following sections. In a first step, I review the main elements of a theory continuum. In a second step, I discuss the respective contribution of the previously identified case study research designs to the theory continuum.

3 Elements of a theory continuum

What a theory is and what a theory is not is a classic debate (Sutton and Staw 1995 ; Weick 1995 ). Often, theories are described in terms of understanding relationships between phenomena which have not been or were not well understood before (Chiles 2003 ; Edmondson and McManus 2007 ; Shah and Corley 2006 ), but there is no overall acceptance as to what constitutes a theory. Theory can be seen as a final product or as a continuum, and there is an ongoing effort to define different stages of this continuum (Andersen and Kragh 2010 ; Colquitt and Zapata-Phelan 2007 ; Edmondson and McManus 2007 ; Snow 2004 ; Swedberg 2012 ). In the following section, basic elements of the theory and the construction of the theory continuum are outlined.

3.1 Basic elements of a theory

Most of the debate concerning what a theory is comprises three basic elements (Alvesson and Kärreman 2007 ; Bacharach 1989 ; Dubin 1978 ; Kaplan 1998 ; Suddaby 2010 ; Weick 1989 , 1995 ; Whetten 1989 ). A theory comprises components (concepts and constructs), used to identify the necessary elements of the phenomenon under investigation. The second is relationships between components (concepts and constructs), explaining the how and whys underlying the relationship. Third, temporal and contextual boundaries limit the generalizability of the theory. As a result, definitions of theory emphasize these components, relationships, and boundaries:

“It is a collection of assertions, both verbal and symbolic, that identifies what variables are important for what reasons, specifies how they are interrelated and why, and identifies the conditions under which they should be related or not related” (Campbell 1990 : 65).

“… a system of constructs and variables in which the constructs are related to each other by propositions and the variables are related to each other by hypotheses” (Bacharach 1989 : 498).

“Theory is about the connections among phenomena, a story about why acts, events, structure, and thoughts occur. Theory emphasizes the nature of causal relationships, identifying what comes first as well as the timing of such events” (Sutton and Staw 1995 : 378).

“… theory is a statement of concepts and their interrelationships that shows how and/or why a phenomenon occurs” (Corley and Gioia 2011 : 12).

The terms “constructs” and “concepts” are either used interchangeably or with different meanings. Positivists use “constructs” as a lens for the observation of a phenomenon (Suddaby 2010 ). Such constructs have to be operationalized and measured. Non-positivists often use the term “concept” as a more value neutral term in place of the term construct (Gioia et al. 2013 ; Suddaby 2010 : 354). Non-positivists aim at developing concepts on the basis of data that contain richness and complexity of the observed phenomenon instead of narrow definitions and operationalizations of constructs. Gioia et al. ( 2013 : 16) clarify the demarcation line between constructs and concepts as follows: “By ‘concept,’ we mean a more general, less well-specified notion capturing qualities that describe or explain a phenomenon of theoretical interest. Put simply, in our way of thinking, concepts are precursors to constructs in making sense of organizational worlds—whether as practitioners living in those worlds, researchers trying to investigate them, or theorists working to model them”.

In sum, theories are a systematic combination of components and their relationships within boundaries. The use of the terms constructs and concepts is related to different philosophical assumptions reflected in different types of case study designs.

3.2 Theory continuum

Weick ( 1995 ) makes an important point that theory is more a continuum than a product. In his view, theorizing is a process containing assumptions, accepted principles, and rules of procedures to explain or predict the behavior of a specified set of phenomena. In similar vein, Gilbert and Christensen ( 2005 ) demonstrate the process character of theory. In their view, a first step of theory building is a careful description of the phenomena. Having already observed and described the phenomena, researchers then classify the phenomena into similar categories. In this phase a framework defines categories and relationships amongst phenomena. In the third phase, researchers build theories to understand (causal) relationships, and in this phase, a model or theory asserts what factors drive the phenomena and under what circumstances. The categorization scheme enables the researchers to predict what they will observe. The “test” offers a confirmation under which circumstances the theory is useful. The early drafts of a theory may be vague in terms of the number and adequateness of factors and their relationships. At the end of the continuum, there may be more precise variables and predicted relationships. These theories have to be extended by boundaries considering time and space.

Across that continuum, different research strategies have various strengths. Several classifications in the literature intend to match research strategies to the different phases of a theory continuum (Andersen and Kragh 2010 ; Colquitt and Zapata-Phelan 2007 ; Edmondson and McManus 2007 ; Snow 2004 ; Swedberg 2012 ). These classifications, although there are differences in terms, comprise three phases with distinguishable characteristics.

3.2.1 Building theory

Here, the careful description of the phenomena is the starting point of theorizing. For example, Snow ( 2004 ) puts this phase as theory discovery, where analytic understandings are generated by means of detailed examination of data. Edmondson and McManus ( 2007 ) state the starting phase of a theory as nascent theory providing answers to new questions revealing new connections among phenomena. Therefore, research questions are open and researchers avoid hypotheses predicting relationships between variables. Swedberg ( 2012 ) highlights the necessity of observation and extensive involvement with the phenomenon at the early stage of theory-building. It is an attempt to understand something of interest by observing and interpreting social facts. Creativity and inspiration are necessary conditions to put observations into concepts and outline a tentative theory.

3.2.2 Developing theory

This tentative theory exists in the second phase of the continuum and has to be developed. Several possibilities exist. In theory extension, the preexisting constructs are extended to other groups or other contexts. In theoretical refinement, a modification of existing theoretical perspectives is conducted (Edmondson and McManus ( 2007 ). New antecedents, moderators, mediators, and outcomes are investigated, enhancing the explanation power of the tentative theory.

3.2.3 Test of theories

Constructs and relationships are well developed to a mature state; measures are precise and operationalized. Such theories are empirically tested with elaborate methods, and research questions are more precise. In the quantitative realm, testing of hypotheses is conducted and statistical analysis is the usual methodological foundation. Recently, researchers criticize that testing theories has become the major focus of scientists today (Delbridge and Fiss 2013 ); testing theories does not only happen to mature theory but to intermediate theory as well. The boundary between theory development and theory testing is not always so clear. While theory development is adding new components to a theory and elaborating the measures, testing a theory implies precise measures, variables, and predicted relationships considering time and space (Gilbert and Christensen ( 2005 ). It will be of interest whether case studies are eligible to test theories as well.

To summarize: there is a conversation as to where on a continuum of theory development, various methods are required to target different contributions to theory (methodological fit). In this discussion, case study research designs have been discussed as a homogeneous set that mostly contributes to theory-building in an exploratory manner. Hence, what is missing is a more differentiated analysis of how case study methodology fits into this conversation, particularly how case study research methodologically fits theory development and theory testing beyond its widely assumed explorative role. In the following section, the above types of case study research designs will be discussed with regard to their positions across the theory continuum.

This distinction adds to existing literature by demonstrating that case study research does not only contribute to theory-building, but also to the development of tentative theories and to the testing of theories. This distinction leads to the next question: is there any interplay between case study research designs and their contributions to the theory continuum? This paper aims at reconciling this interplay with regard to case study design by mirroring phases of a theory continuum with specific types of case study research designs as outlined above. The importance of the interplay between theory and method lies in the capacity to generate and shape theory, while theory can generate and shape method. “In this long march, theory and method surely matter, for they are the tools with which we build both our representations and understandings of organizational life and our reputations” (van Maanen et al. 2007 : 1145). Theory is not the same as methods, but a relationship of this interplay can broaden or restrict both parts of the equation (Swedberg 2012 : 7).

In the following, I discuss how the above-delineated case study research designs unfold their capacities and contribute differently to the theory continuum to build, develop, and test theory.

4 Discussion of the contribution of case study research to a theory continuum

Case study research is diverse with distinct contributions to the continuum of theory. The following table provides the main differences in terms of contributions to theory and specifically locates the case study research designs on the theory continuum (Table  2 ).

In the following, I outline how these specific contributions of case study designs provide better opportunities to enhance the rigor of building theory, developing theory, testing, and reconstructing theory.

4.1 Building theory

In building theory, the phenomenon is new or not understood so far. There is no theory which explains the phenomenon. At the very beginning of the theory continuum, there is curiosity in the phenomenon itself. I focus on the intrinsic case study design which is located in the social construction of reality approach on the very early phase of the theory continuum, as intrinsic case study research design is not theory-building per se but curiosity in the case itself. It is not the purpose of the intrinsic case study to identify abstract concepts and relationships; the specific research strategy lies in the observation and description of a case and the primary method is observation, enabling understanding from personal and vicarious experience. This meets long lasting complaints concerning the lack of (new) theory in management and organization research and signals that the gap between research and management practice is growing. It is argued that the complexity of the reality is not adequately captured (Suddaby et al. 2011 ). It is claimed that management and organization research systematically neglect the dialogue with practice and, as a result, miss new trends or recognize important trends with delay (Corley and Gioia 2011 ).

The specific case study research design’s contribution to theory is in building concrete, context-dependent knowledge with regard to the identification of new phenomena and trends. Openness with regard to the new phenomena, avoiding theoretical preconceptions but building insights out of data, enables the elaboration of meanings and the construction of realities in intrinsic case studies. Intrinsic case studies will enhance the understanding by researcher and reader concerning new phenomena.

The “No Theory First” case study research design is a classic and often cited candidate for building theory. As the phenomenon is new and in the absence of a theory, qualitative data are inspected for aggregation and interpretation. In instrumental case study design, a number of cases will increase the understanding and support building theories by description, aggregation, and interpretation (Stake 2000 ). New themes and concepts are revealed by case descriptions, interviews, documents, and observations, and the analysis of the data enables the specific contribution of the case study design through a constructivist perspective in theory-building.

Although the design by Eisenhardt ( 1989 ) stems from other philosophical assumptions and there are variations and developments in this design, there is still an overwhelming tendency to quote and to stick to her research strategy which aims developing new constructs and new relationships out of real-life cases. Data are collected mainly by interviews, documents, and observations. From within-site analysis and cross-case analysis, themes, concepts, and relationships emerge. Shaping hypotheses comprises: “… refining the definition of the construct and (…) building evidence which measures the construct in each case” (Eisenhardt 1989 : 541). Having identified the emerged constructs, the emergent relationships between constructs are verified in each case. The underlying logic is validation by replication. Cases are treated as experiments in which the hypotheses are replicated case by case. In replication logic cases that confirm the emergent relationships enhance confidence in the validity of the relationships. Disconfirmation of the relationships leads to refinement of the theory. This is similar to Yin’s replication logic, but targets the precision and measurement of constructs and the emerging relationships with regard to the emerging theory. The building of a theory concludes in an understanding of the dynamics underlying the relationship; the primary theoretical reasons for why the relationships exist (Huy 2012 ). Finally, a visual theory with “boxes and arrows” (Eisenhardt and Graebner 2007 ) may visually demonstrate the emerged theory. The theory-building process is finalized by iterating case data, emerging theory, and extant literature.

The “No Theory First” and “Social Construction of Reality” case study research designs, although they represent different philosophical assumptions, adequately fit the theory-building phase concerning new phenomena. The main contribution of case study designs in this phase of the theory continuum lies in the generation of tentative theories.

Case studies at this point of the theory continuum, therefore, have to demonstrate: why the phenomenon is new or of interest; that no previous theory that explains the phenomenon exists; how and why detailed descriptions enhance the understanding of the phenomenon; and how and why new concepts (constructs) and new relationships will enhance our understanding of the phenomenon.

As a result, it has to be demonstrated that the research strategy is in sync with an investigation of a new phenomenon, building a tentative theory.

4.2 Developing theory

In the “Gaps and Holes” case study research design, the phenomenon is partially understood. There is a tentative theory and the research strategy is theory driven. Compared to the theory-building phase, the existence and not the development of propositions differentiate this design along the continuum. The prediction comes first, out of an existing theory. The research strategy and the data have to be confronted by pattern-matching. Pattern-matching is a means to compare the theoretically based predictions with the data in the site: “For case study analysis, one of the most preferred techniques is to use a pattern-matching logic. Such a logic (…) compares an empirically based pattern–that is, one based on the findings from your case study–with a predicted one made before you collected your data (….)” (Yin 2014 : 143). The comparison of propositions and the rich case material is the ground for new elements or relationships within the tentative theory.

Such findings aim to enhance the scientific usefulness of the theory (Corley and Gioia 2011 ). To enhance the validity of the new elements or relationships of the tentative theory, literal replication is a means to confirm the new findings. By that, the theory is developed by new antecedents, moderators, mediators, or outcomes. This modification or extension of the theory contributes to the analytical generalization of the theory.

If new cases provide similar results, the search for regularities is based on more solid ground. Therefore, the strength of case study research in “Gaps and Holes” lies in search for mechanisms in their specific context which can reveal causes and effects more precisely.

The “Gaps and Holes” case study research design is an adequate candidate for this phase of the theory continuum. Case studies at this point of the theory continuum, therefore, have to outline the tentative theory; to demonstrate the lacks and gaps of the tentative theory; to specify how and why the tentative theory is aimed to be extended and/or modified; to develop theoretically based propositions which guide the investigation; and to evaluate new elements, relationships, and mechanisms related to the previous theory (analytical generalization).

As a result and compared to theory-building, a different research strategy exists. While in theory building the research strategy is based on the eliciting of concepts (constructs) and relationships out of data, in theory development, it has to be demonstrated that the research strategy aims to identify new elements and relationships within a tentative theory, identifying mechanisms which explain the phenomenon more precisely.

4.3 Test of theory

In “Gaps and Holes” and “Anomalies”, an extended theory exists. The phenomenon is understood. There is no search for additional components or relationships. Mechanisms seem to explain the functioning or processes of the phenomenon. The research strategy is focused on testing whether the theory holds under different circumstances or under different conditions. Such a test of theories is mainly the domain of experimental and quantitative studies. It is based on previously developed constructs and variables which are the foundation for stating specific testable hypotheses and testing the relations on the basis of quantitative data sets. As a result, highly sophisticated statistical tools enable falsification of the theory. Therefore, testing theory in “Gaps and Holes” is restricted on specific events.

Single case can serve as a test. There is a debate in case study research whether the test of theories is related to the falsification logic of Karl Popper (Flyvbjerg 2006 ; Tsang 2013 ). Another stream of the debate is related to theoretical generalizability (Hillebrand et al. 2001 ; Welch et al. 2011 ). More specifically, test in” Gaps and Holes” is analogous to a single experiment if a single case represents a critical case. If the theory has specified a clear set of propositions and defines the exact conditions within which the theory might explain the phenomena under investigation, a single case study, testing the theory, can confirm or challenge the theory. In sum Yin states: “Overall, the single-case design is eminently justifiable under certain conditions—where the case represents (a) a critical test of existing theory, …” (Yin 2014 : 56). In their survey in the field of International Business, Welch et al. conclude: “In addition, the widespread assumption that the role of the case study lies only in the exploratory, theory-building phase of research downplays its potential to propose causal mechanisms and linkages, and test existing theories” (Welch et al. 2011 : 755).

In multiple case studies, a theoretical replication is a test of theory by comparing the findings with new cases. If a series of cases have revealed pattern-matching between propositions and the data, theoretical replication can be revealed by new waves of cases with contrasting propositions. If the contrasting propositions reveal contrasting results, the findings of the first wave are confirmed. Several possibilities exist to test the initial findings of multiple case studies using different lenses from inside and outside the management realm (Corley and Gioia 2011 ; LePine and Wilcox-King 2010 ; Okhuysen and Bonardi 2011 ; Zahra and Newey 2009 ), but have not become a standard in case study research.

In rival explanations, rival theoretical propositions are developed as a test of the previous theory. This can be distinguished from theoretical replication where contrasting propositions aim to confirm the initial findings. This can, as well, be distinguished from developing theory where rival explanations might develop theory by the elimination of possible influences (interventions, implementations). The rich data enable one to identify internal and external interventions that might be responsible for the findings. Alternative explanations in a new series of cases enable to test, whether a theory “different from the original theory explains the results better (…)” (Yin 2014 : 141).

As a result, it astonishes that theoretical replication and rival explanations, being one of the strengths of case study research, are rarely used. Although the general debate about “lenses” has informed the discussion about theory contributions, this paper demonstrates that there is a wide range of possible integration of vertical or horizontal lenses in case study research design. Case study research designs aiming to test theories have to outline modes of replication and the elimination of rival explanations.

The “anomaly approach” is placed in the final phase of the theory testing, as well. In this approach, a theory exists, but the theory fails to explain anomalies. Burawoy goes a step further. While Yin ( 2014 ) sees a critical case as a test that challenges or contradicts a well formulated theory, in Burawoy’s approach, in contrast to falsification logic (Popper 2002 ), the theory is not rejected but reconstructed. Burawoy relates extended case study design to society and history. Existing theory is challenged by intervention into the social field. Identifying processes of historical roots and social circumstances and considering external forces by structuration lead to the reconstruction of the theory.

It is surprising that this design has been neglected so far in management research. Is there no need to reflect social tensions and distortions in management research? While case study research has, per definition, to investigate phenomena in its natural environment, it is hard to understand why this design has widely been ignored in management and organization research. As a result, testing theory in case study research has to demonstrate that an extended theory exists; a critical case or an anomaly can challenge the theory; theoretical replication and rival explanations will be means to contradict or confirm the theory; and societal circumstances and external forces explain the anomaly.

Compared to theory-building (new concepts/constructs and relationships out of data) and theory development (new elements and relationships within a tentative theory), testing theory challenges extended theory by empirical investigations into failures and anomalies that the current theory cannot explain.

5 Conclusion

Case studies provide a better understanding of phenomena regarding concrete context-dependent knowledge (Andersen and Kragh 2010 ; Flyvbjerg 2006 : 224), but as literature reviews indicate, there is still confusion regarding the adequate utilization of case study methodology (Welch et al. 2011 ). This can be interpreted in a way that authors and even reviewers are not always aware of the methodological fit in case study research. Case study research is mainly narrowed to its “explorative” function, neglecting the scope of possibilities that case study research provides. The claim for more homogeneity of specified rules in case study research misses the important aspect that a method is not a means in itself, but aims at providing improved theories (van Maanen et al. 2007 ). This paper contributes to the fit of case study research designs and the theory continuum regarding the following issues.

5.1 Heterogeneity of case study designs

Although case study research, overall, has similar characteristics, it incorporates various case study research designs that have heterogeneous theoretical goals and use various elements to reach these goals. The analysis revealed that the classical understanding, whereby case study research is adequate for the “exploration” of a theory and quantitative research is adequate for “testing” theory, is oversimplified. Therefore, the theoretical goals of case study research have to be outlined precisely. This study demonstrates that there is variety of case study research designs that have thus far been largely neglected. Case study researchers can utilize the entire spectrum, but have to consider how the phenomenon is related to the theory continuum.

Case study researchers have to demonstrate how they describe new or surprising phenomena, develop new constructs and relationships, add constructs (variables), antecedents, outcomes, moderators, or mediators to a tentative theory, challenge a theory by a critical case, theoretical replication or discarding rival explanations, and reconstruct a theory by tracking failures and anomalies to external circumstances.

5.2 Methodological fit

The rigor of the case study can be enhanced by considering the specific contribution of various case study research designs in each phase of the theory continuum. This paper provides a portfolio of case study research designs that enables researchers and reviewers to evaluate whether the case study arsenal has been adequately located:

At an early phase of the theory continuum, case studies have their strengths in rich descriptions and investigations into new or surprising empirical phenomena and trends. Researchers and readers can benefit from such rich descriptions in understanding and analyzing these phenomena.

Next, on the theory continuum, there is the well-known contribution of case study research in building tentative theory by eliciting constructs or concepts and their relationships out of data.

Third, development of theories is strongly related to literal replication. Strict comparisons, on the one hand, and controlled theoretical advancement, on the other hand, enable the identification of mechanisms, strengthen the notions of causality, and provide generalizable statements.

Fourth, there are specific circumstances under which case study approaches enable one to test theories. This is to confront the theory with a critical case, to test findings of pattern-matching by theoretical replication and discarding rival explanations. Therefore, “Gaps and Holes” provide the opportunity for developing and testing theories through case study design on the theory continuum.

Finally, testing and contradicting theory are not the final rejection of a theory, but is the basis for reconstructing theory by means of case study design. Anomalies can be traced to historical sources, social processes, and external forces.

This paper demonstrates that the precise interplay of case study research designs and theory contributions on the theory continuum is a prerequisite for the contribution of case study research to better theories. If case study research design is differentiated from qualitative research, the intended contribution to theory is stated and designs that fit the aimed contribution to theory are outlined and substantiated; this will critically enhance the rigor of case study research.

Alvesson, M., and D. Kärreman. 2007. Constructing mystery: Empirical matters in theory development. Academy of Management Review 32: 1265–1281.

Article   Google Scholar  

Andersen, P.H., and H. Kragh. 2010. Sense and sensibility: two approaches for using existing theory in theory-building qualitative research. Industrial Marketing Management 39: 49–55.

Bacharach, S.B. 1989. Organizational theories: some criteria for evaluation. Academy of Management Review 14: 496–515.

Google Scholar  

Baxter, P., and S. Jack. 2008. Qualitative case study methodology: study design and implementation for novice researchers. The Qualitative Report 13: 544–559.

Bingham, C.B., and K.M. Eisenhardt. 2011. Rational heuristics: the ‘simple rules’ that strategists learn from process experience. Strategic Management Journal 32: 1437–1464.

Burawoy, M. 1991. Ethnography unbound . Power and resistance in the modern metropolis: University of California Press.

Burawoy, M. 1998. The extended case method. Sociological Theory 16: 4–33.

Burawoy, M. 2009. The extended case method. Four countries, four decades, four great transformations, and one theoretical tradition . Berkeley: University of California Press.

Burns, R.B. 2000. Introduction to research methods . United States of America: SAGE publications.

Campbell, J.P. 1990. The role of theory in industrial and organizational psychology. In Handbook of industrial and organizational psychology , 2nd ed, ed. M.D. Dunnette, L.M. Hough, and H.C. Triandis, 39–73. Palo Alto: Consulting Psychologists Press.

Chiles, T.H. 2003. Process theorizing: too important to ignore in a kaleidic world. Academy of Management Learning & Education 2: 288–291.

Colquitt, J.A., and C.P. Zapata-Phelan. 2007. Trends in theory building and theory testing: a five-decade study of the Academy of Management Journal. Academy of Management Journal 50: 1281–1303.

Corbin, J.M., & Strauss, A.L. 2015. Basics of qualitative research: Techniques and procedures for developing grounded theory (4th ed). Los Angeles, CA: Sage Publications.

Corley, K.G., and D.A. Gioia. 2011. Building theory about theory building: what constitutes a theoretical contribution? Academy of Management Review 36: 12–32.

Danneels, E. 2011. Trying to become a different type of company: dynamic capability at Smith Corona. Strategic Management Journal 32: 1–31.

Davis, J.P., and K.M. Eisenhardt. 2011. Rotating leadership and collaborative innovation: recombination processes in symbiotic relationships. Administrative Science Quarterly 56: 159–201.

Delbridge, R., and P.C. Fiss. 2013. Editors’ comments: styles of theorizing and the social organization of knowledge. Academy of Management Review 38: 325–331.

Dooley, L.M. 2002. Case study research and theory building. Advances in Developing Human Resources 4: 335–354.

Dubin, R. 1978. Theory building . New York: Free Press.

Dyer, W.G., and A.L. Wilkins. 1991. Better stories, not better constructs, to generate better theory: a rejoinder to Eisenhardt. Academy of Management Review 16: 613–619.

Edmondson, A.C., and S.E. McManus. 2007. Methodological fit in management field research. Academy of Management Review 32: 1155–1179.

Eisenhardt, K.M. 1989. Building theories from case study research. Academy of Management Review 14: 532–550.

Eisenhardt, K.M. 1991. Better stories and better constructs: The case for rigor and comparative logic. Academy of Management Review , 16(3): 620–627.

Eisenhardt, K.M., and M.E. Graebner. 2007. Theory building from cases: opportunities and challenges. Academy of Management Journal 50: 25–32.

Ellonen, H.K., P. Wikström, and A. Jantunen. 2009. Linking dynamic-capability portfolios and innovation outcomes. Technovation 29: 753–762.

Fiss, P.C. 2009. Case studies and the configurational analysis of organizational phenomena. In The SAGE handbook of case-based methods , ed. D.S. Byrne, and C.C. Ragin, 424–440. London/Thousand Oaks: SAGE.

Chapter   Google Scholar  

Flick, U. 2009. An introduction to qualitative research , 4th ed. London: SAGE.

Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry , 12(2): 219–245.

Gilbert, C.G., and C.M. Christensen. 2005. Anomaly-seeking research: thirty years of development in resource allocation theory. In From resource allocation to strategy , ed. J.L. Bower, and C.G. Gilbert, 71–89. Oxford: University Press, Oxford.

Gioia, D.A., K.G. Corley, and A.L. Hamilton. 2013. Seeking qualitative rigor in inductive research: notes on the Gioia methodology. Organizational Research Methods 16: 15–31.

Gomm, R., M. Hammersley, and P. Foster. 2000. Case study method. Key issues, key texts . London/Thousand Oaks: Sage Publications.

Greenwood, R., and R. Suddaby. 2006. Institutional entrepreneurship in mature fields: the big five accounting firms. Academy of Management Journal 49: 27–48.

Guba, E.G., and Y.S. Lincoln. 2005. Paradigmatic controversies, contradictions, and emerging confluences. In The SAGE handbook of qualitative research , 3rd ed, ed. N.K. Denzin, and Y.S. Lincoln, 191–215. London, Thousand Oaks: Sage Publications.

Hillebrand, B., R.A.W. Kok, and W.G. Biemans. 2001. Theory-testing using case studies: a comment on Johnston, Leach, and Liu. Industrial Marketing Management 30: 651–657.

Huy, Q.N. 2012. Improving the odds of publishing inductive qualitative research in Premier Academic Journals. The Journal of Applied Behavioral Science 48: 282–287.

Ituma, A., R. Simpson, F. Ovadje, N. Cornelius, and C. Mordi. 2011. Four ‘domains’ of career success: how managers in Nigeria evaluate career outcomes. The International Journal of Human Resource Management 22: 3638–3660.

Kaplan, A. 1998. The conduct of inquiry. Methodology for behavioral science . New Brunswick, N.J.: Transaction Publishers.

Lee, T.W. 1999. Using qualitative methods in organizational research. Organizational research methods series . Thousand Oaks: Sage Publications.

LePine, J.A., and A. Wilcox-King. 2010. Editors´s comments: developing novel theoretical insight from reviews of existing rheory and research. Academy of Management Review 35: 506–509.

Mason, J. 2002. Qualitative researching , 2nd ed. London, Thousand Oaks: Sage Publications.

Miles, M.B., Huberman, M.A., & Saldana, J. 2014. Qualitative data analysis: A methods sourcebook (3rd ed). Los Angeles, CA: Sage Publications.

Okhuysen, G., and J.P. Bonardi. 2011. The challenges of building theory by combining lenses. Academy of Management Review 36: 6–11.

Piekkari, R., C. Welch, and E. Paavilainen. 2009. The case study as disciplinary convention: evidence from international business journals. Organizational Research Methods 12: 567–589.

Popper, K.R. 2002. Logik der Forschung . Tübingen: Mohr Siebeck.

Ravenswood, K. 2011. Eisenhardt’s impact on theory in case study research. Journal of Business Research 64: 680–686.

Ridder, H.G. 2016. Case study research. Approaches, methods, contribution to theory. Sozialwissenschaftliche Forschungsmethoden , vol. 12. München/Mering: Rainer Hampp Verlag.

Schwandt, T.A. 1994. Constructivist, interpretivist approaches to human inquiry. In Handbook of qualitative research , ed. N.K. Denzin, and Y.S. Lincoln, 118–137. Thousand Oaks: Sage Publications.

Shah, S.K., and K.G. Corley. 2006. Building better theory by bridging the quantitative–qualitative divide. Journal of Management Studies 43: 1821–1835.

Shane, S. 2000. Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science 11: 448–469.

Snow, C.C. 2004. Thoughts on alternative pathways to theoretical development: Theory generation, extension, and refinement. In Workshop on scientific foundations of qualitative research , ed. C.C. Ragin, J. Nagel, and P. White, 133–136. Arlington, VA: National Science Foundation.

Stake, R.E. 1995. The art of case study research . London, Thousand Oaks: Sage Publications.

Stake, R.E. 2000. The case study and generalizability. In Case study method. Key issues, key texts , ed. R. Gomm, M. Hammersley, and P. Foster, 19–26. London/Thousand Oaks: Sage Publications.

Stake, R.E. 2005. Qualitative case studies. In The SAGE handbook of qualitative research , 3rd ed, ed. N.K. Denzin, and Y.S. Lincoln, 443–466. London, Thousand Oaks: Sage Publications.

Suddaby, R. 2010. Editor’s comments: construct clarity in theories of management and organization. Academy of Management Review 35: 346–357.

Suddaby, R., C. Hardy, and Q.N. Huy. 2011. Introduction to special topic forum: where are the new theories of organization? Academy of Management Review 36: 236–246.

Sutton, R.I., and B.M. Staw. 1995. What theory is not. Administrative Science Quarterly 40: 371–384.

Swedberg, R. 2012. Theorizing in sociology and social science: turning to the context of discovery. Theory and Society 41: 1–40.

Tsang, E.W.K. 2013. Generalizing from research findings: the merits of case studies. International Journal of Management Reviews 16: 369–383.

van Maanen, J., J.B. Sørensen, and T.R. Mitchell. 2007. The interplay between theory and method. Academy of Management Review 32: 1145–1154.

Vaughan, D. 1992. Theory elaboration: The heuristics of case analysis. In What is a case? , ed. C.C. Ragin, and H.S. Becker, 173–202. Exploring the foundations of social inquiry: Cambridge University Press, Cambridge, New York.

Wadham, H., and R.C. Warren. 2014. Telling organizational tales the extended case method in practice. Organizational Research Methods 17: 5–22.

Weick, K.E. 1989. Theory construction as disciplined imagination. Academy of Management Review 14: 516–531.

Weick, K.E. 1995. What theory is not, theorizing is. Administrative Science Quarterly 40: 385–390.

Welch, C., R. Piekkari, E. Plakoyiannaki, and E. Paavilainen-Mäntymäki. 2011. Theorising from case studies: towards a pluralist future for international business research. Journal of International Business Studies 42: 740–762.

Whetten, D.A. 1989. What constitutes a theoretical contribution? Academy of Management Review 14: 490–495.

Yin, R.K. 2014. Case study research. Design and methods , 5th ed. London, Thousand Oaks: Sage Publications.

Zahra, S.A., and L.R. Newey. 2009. Maximizing the Impact of Organization Science: theory-Building at the Intersection of Disciplines and/or Fields. Journal of Management Studies 46: 1059–1075.

Download references

Author information

Authors and affiliations.

Institute of Human Resource Management, Leibniz University of Hannover, Königsworther Platz 1, 30167, Hannover, Germany

Hans-Gerd Ridder

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Hans-Gerd Ridder .

Additional information

I thank two anonymous reviewers for their constructive comments. I am grateful for valuable thoughts generously provided by Ann Kristin Zobel.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Ridder, HG. The theory contribution of case study research designs. Bus Res 10 , 281–305 (2017). https://doi.org/10.1007/s40685-017-0045-z

Download citation

Received : 26 January 2016

Accepted : 06 February 2017

Published : 16 February 2017

Issue Date : October 2017

DOI : https://doi.org/10.1007/s40685-017-0045-z

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

• Case studies
• Research design
• Heterogeneity of case study designs
• Theory continuum
• Methodological fit
• Contribution to theory
• Find a journal
• Publish with us
• Track your research

Qualitative Descriptive Research and Case Study Research: A Comparison

Applying qualitative research to the field of nursing allows shedding light on various issues such as the nature of specific phenomena, the perception of a problem by nurses and patients, and other concerns. Qualitative research encompasses a range of methods for the analysis of key variables, which define the focus and the tools used in the process (Holly, 2013). The adoption of qualitative descriptive research and a case study are typically seen as the most common options (Dang & Dearholt, 2017). However, although the described methods may seem similar, the differences in the approaches that they suggest define the specifics of their application. While a qualitative case study implies mostly general analysis of observations and documentation, qualitative descriptive research requires a precise focus on a particular group of participants that will be studied.

Similarly, qualitative descriptive research is quite different from the research method known as action research. In contrast to qualitative descriptive research, action research implies taking field notes while collecting the necessary data within a focus group (Holly, 2013). Therefore, it could be argued that the focus of action research is slightly narrower than the one of qualitative descriptive research (Singla, Jones, Edwards, & Kumar, 2015). Nevertheless, both approaches toward the analysis of variables require access to essential documentation and patient records to perform a comprehensive assessment of the key factors. Despite being quite similar in their basic premise of studying qualitative relationships between key variables, the existing types of qualitative research suggest different types of assessment and perspectives (Holly, 2013). Thus, the choice of a qualitative research method hinges on study objectives, types of relationships that it seeks to explore, and other factors.

Dang, D., & Dearholt, S. L. (2017). Johns Hopkins nursing evidence-based practice: Model and guidelines (3rd ed.). New York, NY: Sigma Theta Tau.

Holly, C. (Ed.). (2013). Scholarly inquiry and the DNP Capstone . New York, NY: Springer Publishing Company.

Singla, M., Jones, M., Edwards, I., & Kumar, S. (2015). Physiotherapists’ assessment of patients’ psychosocial status: Are we standing on thin ice? A qualitative descriptive study. Manual Therapy, 20 (2), 328-334. Web.

Cite this paper

• Chicago (N-B)
• Chicago (A-D)

StudyCorgi. (2021, July 21). Qualitative Descriptive Research and Case Study Research: A Comparison. https://studycorgi.com/qualitative-descriptive-research-and-case-study-research-a-comparison/

"Qualitative Descriptive Research and Case Study Research: A Comparison." StudyCorgi , 21 July 2021, studycorgi.com/qualitative-descriptive-research-and-case-study-research-a-comparison/.

StudyCorgi . (2021) 'Qualitative Descriptive Research and Case Study Research: A Comparison'. 21 July.

1. StudyCorgi . "Qualitative Descriptive Research and Case Study Research: A Comparison." July 21, 2021. https://studycorgi.com/qualitative-descriptive-research-and-case-study-research-a-comparison/.

Bibliography

StudyCorgi . "Qualitative Descriptive Research and Case Study Research: A Comparison." July 21, 2021. https://studycorgi.com/qualitative-descriptive-research-and-case-study-research-a-comparison/.

StudyCorgi . 2021. "Qualitative Descriptive Research and Case Study Research: A Comparison." July 21, 2021. https://studycorgi.com/qualitative-descriptive-research-and-case-study-research-a-comparison/.

This paper, “Qualitative Descriptive Research and Case Study Research: A Comparison”, was written and voluntary submitted to our free essay database by a straight-A student. Please ensure you properly reference the paper if you're using it to write your assignment.

Before publication, the StudyCorgi editorial team proofread and checked the paper to make sure it meets the highest standards in terms of grammar, punctuation, style, fact accuracy, copyright issues, and inclusive language. Last updated: November 11, 2023 .

If you are the author of this paper and no longer wish to have it published on StudyCorgi, request the removal . Please use the “ Donate your paper ” form to submit an essay.

• Key Differences

Know the Differences & Comparisons

Difference Between Exploratory and Descriptive Research

The research design is defined as a framework for carrying out research activities in different fields of study. The research design is classified into two important categories i.e. exploratory and conclusive research. Conclusive research is further subdivided into descriptive and casual research. The people often juxtapose exploratory research and descriptive research, but the fact is that they are different.

Take a read of this article to understand the differences between exploratory and descriptive research.

Content: Exploratory Research Vs Descriptive Research

Comparison chart, definition of exploratory research.

As the name implies, the primary objective of exploratory research is to explore a problem to provide insights into and comprehension for more precise investigation. It focuses on the discovery of ideas and thoughts. The exploratory research design is suitable for studies which are flexible enough to provide an opportunity for considering all the aspects of the problem.

At this point, the required information is loosely defined, and the research process is flexible and unstructured. It is used in the situation when you must define the problem correctly, identify alternative courses of actions, develop a hypothesis, gain additional insights before the development of an approach, set priorities for further examination. The following methods are used for conducting exploratory research

• Survey of concerning literature
• Experience survey
• Analysis of insights stimulating

Definition of Descriptive Research

By the term descriptive research, we mean a type of conclusive research study which is concerned with describing the characteristics of a particular individual or group. It includes research related to specific predictions, features or functions of person or group, the narration of facts, etc.

The descriptive research aims at obtaining complete and accurate information for the study, the method adopted must be carefully planned. The researcher should precisely define what he wants to measure? How does he want to measure? He should clearly define the population under study. It uses methods like quantitative analysis of secondary data, surveys, panels, observations, interviews, questionnaires, etc.

Descriptive Research concentrates on formulating the research objective, designing methods for the collection of data, selection of the sample, data collection, processing, and analysis, reporting the results.

Key Differences Between Exploratory and Descriptive Research

The difference between exploratory and descriptive research can be drawn clearly on the following grounds:

• Research conducted for formulating a problem for more clear investigation is called exploratory research. Research that explore and explains an individual, group or a situation, is called descriptive research.
• The exploratory research aims at the discovery of ideas and thoughts whereas the primary purpose of descriptive research is to describe the characteristics and functions.
• The overall design of the exploratory research should be flexible enough so that it provides an opportunity to consider various aspects of the problem. On the contrary, in descriptive research, the overall design should be rigid which protects against bias and also maximise reliability.
• The research process is unstructured in exploratory research. However, it is structured in the case of descriptive research.
• Non-probability sampling i.e. judgment or purposive sampling design is used in exploratory research. As opposed to descriptive research where probability (random) sampling design is used.
• When it comes to statistical design, exploratory research has no pre-planned design for analysis. Unlike, descriptive research that has the pre-planned design for analysis.

Therefore exploratory research results in insights or hypothesis, regardless of the method adopted, the most important thing is that it should remain flexible so that all the facets of the problem can be studied, as and when they arise. Conversely, descriptive research is a comparative design which is prepared according to the study and resources available. Such study minimises bias and maximises reliability.

You Might Also Like:

adrian says

November 16, 2017 at 6:22 pm

thank you so much for this incredible idea!

privanus simon says

June 20, 2018 at 11:42 am

The material you sent have been so useful in my study,Keep it up To me as as a Masters scholar I appriciate

Prasanna says

December 9, 2018 at 12:48 am

Overall website is superb,well explained.thanks a lot

ALBERT KOPES says

May 8, 2019 at 8:26 am

Thank you so much for your brilliant ideas. I really appreciated your hard work by putting your knowledge.. THANKS

Abinet A. Wanore says

October 11, 2019 at 10:11 pm

Thankyou so much. It is so helpful

Mkhulisi says

August 6, 2020 at 5:21 pm

this content has been very useful to me

Abdul Wahid Nuristani says

March 15, 2021 at 4:49 pm

great note.

Tina Sales says

January 25, 2022 at 9:02 pm

Thank you very much All information herein are very helpful for my current undertakings.

Sandile says

February 12, 2022 at 1:42 pm

I would like to reference your work, kindly assist

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.