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  • 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.

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Methodology

  • What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, 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 analyze the case, other interesting articles.

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.

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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

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

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.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

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

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

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.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

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 analyze 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.

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

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

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

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Case Studies

This guide examines case studies, a form of qualitative descriptive research that is used to look at individuals, a small group of participants, or a group as a whole. Researchers collect data about participants using participant and direct observations, interviews, protocols, tests, examinations of records, and collections of writing samples. Starting with a definition of the case study, the guide moves to a brief history of this research method. Using several well documented case studies, the guide then looks at applications and methods including data collection and analysis. A discussion of ways to handle validity, reliability, and generalizability follows, with special attention to case studies as they are applied to composition studies. Finally, this guide examines the strengths and weaknesses of case studies.

Definition and Overview

Case study refers to the collection and presentation of detailed information about a particular participant or small group, frequently including the accounts of subjects themselves. A form of qualitative descriptive research, the case study looks intensely at an individual or small participant pool, drawing conclusions only about that participant or group and only in that specific context. Researchers do not focus on the discovery of a universal, generalizable truth, nor do they typically look for cause-effect relationships; instead, emphasis is placed on exploration and description.

Case studies typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located. Thick description also involves interpreting the meaning of demographic and descriptive data such as cultural norms and mores, community values, ingrained attitudes, and motives.

Unlike quantitative methods of research, like the survey, which focus on the questions of who, what, where, how much, and how many, and archival analysis, which often situates the participant in some form of historical context, case studies are the preferred strategy when how or why questions are asked. Likewise, they are the preferred method when the researcher has little control over the events, and when there is a contemporary focus within a real life context. In addition, unlike more specifically directed experiments, case studies require a problem that seeks a holistic understanding of the event or situation in question using inductive logic--reasoning from specific to more general terms.

In scholarly circles, case studies are frequently discussed within the context of qualitative research and naturalistic inquiry. Case studies are often referred to interchangeably with ethnography, field study, and participant observation. The underlying philosophical assumptions in the case are similar to these types of qualitative research because each takes place in a natural setting (such as a classroom, neighborhood, or private home), and strives for a more holistic interpretation of the event or situation under study.

Unlike more statistically-based studies which search for quantifiable data, the goal of a case study is to offer new variables and questions for further research. F.H. Giddings, a sociologist in the early part of the century, compares statistical methods to the case study on the basis that the former are concerned with the distribution of a particular trait, or a small number of traits, in a population, whereas the case study is concerned with the whole variety of traits to be found in a particular instance" (Hammersley 95).

Case studies are not a new form of research; naturalistic inquiry was the primary research tool until the development of the scientific method. The fields of sociology and anthropology are credited with the primary shaping of the concept as we know it today. However, case study research has drawn from a number of other areas as well: the clinical methods of doctors; the casework technique being developed by social workers; the methods of historians and anthropologists, plus the qualitative descriptions provided by quantitative researchers like LePlay; and, in the case of Robert Park, the techniques of newspaper reporters and novelists.

Park was an ex-newspaper reporter and editor who became very influential in developing sociological case studies at the University of Chicago in the 1920s. As a newspaper professional he coined the term "scientific" or "depth" reporting: the description of local events in a way that pointed to major social trends. Park viewed the sociologist as "merely a more accurate, responsible, and scientific reporter." Park stressed the variety and value of human experience. He believed that sociology sought to arrive at natural, but fluid, laws and generalizations in regard to human nature and society. These laws weren't static laws of the kind sought by many positivists and natural law theorists, but rather, they were laws of becoming--with a constant possibility of change. Park encouraged students to get out of the library, to quit looking at papers and books, and to view the constant experiment of human experience. He writes, "Go and sit in the lounges of the luxury hotels and on the doorsteps of the flophouses; sit on the Gold Coast settees and on the slum shakedowns; sit in the Orchestra Hall and in the Star and Garter Burlesque. In short, gentlemen [sic], go get the seats of your pants dirty in real research."

But over the years, case studies have drawn their share of criticism. In fact, the method had its detractors from the start. In the 1920s, the debate between pro-qualitative and pro-quantitative became quite heated. Case studies, when compared to statistics, were considered by many to be unscientific. From the 1930's on, the rise of positivism had a growing influence on quantitative methods in sociology. People wanted static, generalizable laws in science. The sociological positivists were looking for stable laws of social phenomena. They criticized case study research because it failed to provide evidence of inter subjective agreement. Also, they condemned it because of the few number of cases studied and that the under-standardized character of their descriptions made generalization impossible. By the 1950s, quantitative methods, in the form of survey research, had become the dominant sociological approach and case study had become a minority practice.

Educational Applications

The 1950's marked the dawning of a new era in case study research, namely that of the utilization of the case study as a teaching method. "Instituted at Harvard Business School in the 1950s as a primary method of teaching, cases have since been used in classrooms and lecture halls alike, either as part of a course of study or as the main focus of the course to which other teaching material is added" (Armisted 1984). The basic purpose of instituting the case method as a teaching strategy was "to transfer much of the responsibility for learning from the teacher on to the student, whose role, as a result, shifts away from passive absorption toward active construction" (Boehrer 1990). Through careful examination and discussion of various cases, "students learn to identify actual problems, to recognize key players and their agendas, and to become aware of those aspects of the situation that contribute to the problem" (Merseth 1991). In addition, students are encouraged to "generate their own analysis of the problems under consideration, to develop their own solutions, and to practically apply their own knowledge of theory to these problems" (Boyce 1993). Along the way, students also develop "the power to analyze and to master a tangled circumstance by identifying and delineating important factors; the ability to utilize ideas, to test them against facts, and to throw them into fresh combinations" (Merseth 1991).

In addition to the practical application and testing of scholarly knowledge, case discussions can also help students prepare for real-world problems, situations and crises by providing an approximation of various professional environments (i.e. classroom, board room, courtroom, or hospital). Thus, through the examination of specific cases, students are given the opportunity to work out their own professional issues through the trials, tribulations, experiences, and research findings of others. An obvious advantage to this mode of instruction is that it allows students the exposure to settings and contexts that they might not otherwise experience. For example, a student interested in studying the effects of poverty on minority secondary student's grade point averages and S.A.T. scores could access and analyze information from schools as geographically diverse as Los Angeles, New York City, Miami, and New Mexico without ever having to leave the classroom.

The case study method also incorporates the idea that students can learn from one another "by engaging with each other and with each other's ideas, by asserting something and then having it questioned, challenged and thrown back at them so that they can reflect on what they hear, and then refine what they say" (Boehrer 1990). In summary, students can direct their own learning by formulating questions and taking responsibility for the study.

Types and Design Concerns

Researchers use multiple methods and approaches to conduct case studies.

Types of Case Studies

Under the more generalized category of case study exist several subdivisions, each of which is custom selected for use depending upon the goals and/or objectives of the investigator. These types of case study include the following:

Illustrative Case Studies These are primarily descriptive studies. They typically utilize one or two instances of an event to show what a situation is like. Illustrative case studies serve primarily to make the unfamiliar familiar and to give readers a common language about the topic in question.

Exploratory (or pilot) Case Studies These are condensed case studies performed before implementing a large scale investigation. Their basic function is to help identify questions and select types of measurement prior to the main investigation. The primary pitfall of this type of study is that initial findings may seem convincing enough to be released prematurely as conclusions.

Cumulative Case Studies These serve to aggregate information from several sites collected at different times. The idea behind these studies is the collection of past studies will allow for greater generalization without additional cost or time being expended on new, possibly repetitive studies.

Critical Instance Case Studies These examine one or more sites for either the purpose of examining a situation of unique interest with little to no interest in generalizability, or to call into question or challenge a highly generalized or universal assertion. This method is useful for answering cause and effect questions.

Identifying a Theoretical Perspective

Much of the case study's design is inherently determined for researchers, depending on the field from which they are working. In composition studies, researchers are typically working from a qualitative, descriptive standpoint. In contrast, physicists will approach their research from a more quantitative perspective. Still, in designing the study, researchers need to make explicit the questions to be explored and the theoretical perspective from which they will approach the case. The three most commonly adopted theories are listed below:

Individual Theories These focus primarily on the individual development, cognitive behavior, personality, learning and disability, and interpersonal interactions of a particular subject.

Organizational Theories These focus on bureaucracies, institutions, organizational structure and functions, or excellence in organizational performance.

Social Theories These focus on urban development, group behavior, cultural institutions, or marketplace functions.

Two examples of case studies are used consistently throughout this chapter. The first, a study produced by Berkenkotter, Huckin, and Ackerman (1988), looks at a first year graduate student's initiation into an academic writing program. The study uses participant-observer and linguistic data collecting techniques to assess the student's knowledge of appropriate discourse conventions. Using the pseudonym Nate to refer to the subject, the study sought to illuminate the particular experience rather than to generalize about the experience of fledgling academic writers collectively.

For example, in Berkenkotter, Huckin, and Ackerman's (1988) study we are told that the researchers are interested in disciplinary communities. In the first paragraph, they ask what constitutes membership in a disciplinary community and how achieving membership might affect a writer's understanding and production of texts. In the third paragraph they state that researchers must negotiate their claims "within the context of his sub specialty's accepted knowledge and methodology." In the next paragraph they ask, "How is literacy acquired? What is the process through which novices gain community membership? And what factors either aid or hinder students learning the requisite linguistic behaviors?" This introductory section ends with a paragraph in which the study's authors claim that during the course of the study, the subject, Nate, successfully makes the transition from "skilled novice" to become an initiated member of the academic discourse community and that his texts exhibit linguistic changes which indicate this transition. In the next section the authors make explicit the sociolinguistic theoretical and methodological assumptions on which the study is based (1988). Thus the reader has a good understanding of the authors' theoretical background and purpose in conducting the study even before it is explicitly stated on the fourth page of the study. "Our purpose was to examine the effects of the educational context on one graduate student's production of texts as he wrote in different courses and for different faculty members over the academic year 1984-85." The goal of the study then, was to explore the idea that writers must be initiated into a writing community, and that this initiation will change the way one writes.

The second example is Janet Emig's (1971) study of the composing process of a group of twelfth graders. In this study, Emig seeks to answer the question of what happens to the self as a result educational stimuli in terms of academic writing. The case study used methods such as protocol analysis, tape-recorded interviews, and discourse analysis.

In the case of Janet Emig's (1971) study of the composing process of eight twelfth graders, four specific hypotheses were made:

  • Twelfth grade writers engage in two modes of composing: reflexive and extensive.
  • These differences can be ascertained and characterized through having the writers compose aloud their composition process.
  • A set of implied stylistic principles governs the writing process.
  • For twelfth grade writers, extensive writing occurs chiefly as a school-sponsored activity, or reflexive, as a self-sponsored activity.

In this study, the chief distinction is between the two dominant modes of composing among older, secondary school students. The distinctions are:

  • The reflexive mode, which focuses on the writer's thoughts and feelings.
  • The extensive mode, which focuses on conveying a message.

Emig also outlines the specific questions which guided the research in the opening pages of her Review of Literature , preceding the report.

Designing a Case Study

After considering the different sub categories of case study and identifying a theoretical perspective, researchers can begin to design their study. Research design is the string of logic that ultimately links the data to be collected and the conclusions to be drawn to the initial questions of the study. Typically, research designs deal with at least four problems:

  • What questions to study
  • What data are relevant
  • What data to collect
  • How to analyze that data

In other words, a research design is basically a blueprint for getting from the beginning to the end of a study. The beginning is an initial set of questions to be answered, and the end is some set of conclusions about those questions.

Because case studies are conducted on topics as diverse as Anglo-Saxon Literature (Thrane 1986) and AIDS prevention (Van Vugt 1994), it is virtually impossible to outline any strict or universal method or design for conducting the case study. However, Robert K. Yin (1993) does offer five basic components of a research design:

  • A study's questions.
  • A study's propositions (if any).
  • A study's units of analysis.
  • The logic that links the data to the propositions.
  • The criteria for interpreting the findings.

In addition to these five basic components, Yin also stresses the importance of clearly articulating one's theoretical perspective, determining the goals of the study, selecting one's subject(s), selecting the appropriate method(s) of collecting data, and providing some considerations to the composition of the final report.

Conducting Case Studies

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of approaches and methods. These approaches, methods, and related issues are discussed in depth in this section.

Method: Single or Multi-modal?

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of methods. Some common methods include interviews , protocol analyses, field studies, and participant-observations. Emig (1971) chose to use several methods of data collection. Her sources included conversations with the students, protocol analysis, discrete observations of actual composition, writing samples from each student, and school records (Lauer and Asher 1988).

Berkenkotter, Huckin, and Ackerman (1988) collected data by observing classrooms, conducting faculty and student interviews, collecting self reports from the subject, and by looking at the subject's written work.

A study that was criticized for using a single method model was done by Flower and Hayes (1984). In this study that explores the ways in which writers use different forms of knowing to create space, the authors used only protocol analysis to gather data. The study came under heavy fire because of their decision to use only one method.

Participant Selection

Case studies can use one participant, or a small group of participants. However, it is important that the participant pool remain relatively small. The participants can represent a diverse cross section of society, but this isn't necessary.

For example, the Berkenkotter, Huckin, and Ackerman (1988) study looked at just one participant, Nate. By contrast, in Janet Emig's (1971) study of the composition process of twelfth graders, eight participants were selected representing a diverse cross section of the community, with volunteers from an all-white upper-middle-class suburban school, an all-black inner-city school, a racially mixed lower-middle-class school, an economically and racially mixed school, and a university school.

Often, a brief "case history" is done on the participants of the study in order to provide researchers with a clearer understanding of their participants, as well as some insight as to how their own personal histories might affect the outcome of the study. For instance, in Emig's study, the investigator had access to the school records of five of the participants, and to standardized test scores for the remaining three. Also made available to the researcher was the information that three of the eight students were selected as NCTE Achievement Award winners. These personal histories can be useful in later stages of the study when data are being analyzed and conclusions drawn.

Data Collection

There are six types of data collected in case studies:

  • Archival records.
  • Interviews.
  • Direct observation.
  • Participant observation.

In the field of composition research, these six sources might be:

  • A writer's drafts.
  • School records of student writers.
  • Transcripts of interviews with a writer.
  • Transcripts of conversations between writers (and protocols).
  • Videotapes and notes from direct field observations.
  • Hard copies of a writer's work on computer.

Depending on whether researchers have chosen to use a single or multi-modal approach for the case study, they may choose to collect data from one or any combination of these sources.

Protocols, that is, transcriptions of participants talking aloud about what they are doing as they do it, have been particularly common in composition case studies. For example, in Emig's (1971) study, the students were asked, in four different sessions, to give oral autobiographies of their writing experiences and to compose aloud three themes in the presence of a tape recorder and the investigator.

In some studies, only one method of data collection is conducted. For example, the Flower and Hayes (1981) report on the cognitive process theory of writing depends on protocol analysis alone. However, using multiple sources of evidence to increase the reliability and validity of the data can be advantageous.

Case studies are likely to be much more convincing and accurate if they are based on several different sources of information, following a corroborating mode. This conclusion is echoed among many composition researchers. For example, in her study of predrafting processes of high and low-apprehensive writers, Cynthia Selfe (1985) argues that because "methods of indirect observation provide only an incomplete reflection of the complex set of processes involved in composing, a combination of several such methods should be used to gather data in any one study." Thus, in this study, Selfe collected her data from protocols, observations of students role playing their writing processes, audio taped interviews with the students, and videotaped observations of the students in the process of composing.

It can be said then, that cross checking data from multiple sources can help provide a multidimensional profile of composing activities in a particular setting. Sharan Merriam (1985) suggests "checking, verifying, testing, probing, and confirming collected data as you go, arguing that this process will follow in a funnel-like design resulting in less data gathering in later phases of the study along with a congruent increase in analysis checking, verifying, and confirming."

It is important to note that in case studies, as in any qualitative descriptive research, while researchers begin their studies with one or several questions driving the inquiry (which influence the key factors the researcher will be looking for during data collection), a researcher may find new key factors emerging during data collection. These might be unexpected patterns or linguistic features which become evident only during the course of the research. While not bearing directly on the researcher's guiding questions, these variables may become the basis for new questions asked at the end of the report, thus linking to the possibility of further research.

Data Analysis

As the information is collected, researchers strive to make sense of their data. Generally, researchers interpret their data in one of two ways: holistically or through coding. Holistic analysis does not attempt to break the evidence into parts, but rather to draw conclusions based on the text as a whole. Flower and Hayes (1981), for example, make inferences from entire sections of their students' protocols, rather than searching through the transcripts to look for isolatable characteristics.

However, composition researchers commonly interpret their data by coding, that is by systematically searching data to identify and/or categorize specific observable actions or characteristics. These observable actions then become the key variables in the study. Sharan Merriam (1988) suggests seven analytic frameworks for the organization and presentation of data:

  • The role of participants.
  • The network analysis of formal and informal exchanges among groups.
  • Historical.
  • Thematical.
  • Ritual and symbolism.
  • Critical incidents that challenge or reinforce fundamental beliefs, practices, and values.

There are two purposes of these frameworks: to look for patterns among the data and to look for patterns that give meaning to the case study.

As stated above, while most researchers begin their case studies expecting to look for particular observable characteristics, it is not unusual for key variables to emerge during data collection. Typical variables coded in case studies of writers include pauses writers make in the production of a text, the use of specific linguistic units (such as nouns or verbs), and writing processes (planning, drafting, revising, and editing). In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, researchers coded the participant's texts for use of connectives, discourse demonstratives, average sentence length, off-register words, use of the first person pronoun, and the ratio of definite articles to indefinite articles.

Since coding is inherently subjective, more than one coder is usually employed. In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, three rhetoricians were employed to code the participant's texts for off-register phrases. The researchers established the agreement among the coders before concluding that the participant used fewer off-register words as the graduate program progressed.

Composing the Case Study Report

In the many forms it can take, "a case study is generically a story; it presents the concrete narrative detail of actual, or at least realistic events, it has a plot, exposition, characters, and sometimes even dialogue" (Boehrer 1990). Generally, case study reports are extensively descriptive, with "the most problematic issue often referred to as being the determination of the right combination of description and analysis" (1990). Typically, authors address each step of the research process, and attempt to give the reader as much context as possible for the decisions made in the research design and for the conclusions drawn.

This contextualization usually includes a detailed explanation of the researchers' theoretical positions, of how those theories drove the inquiry or led to the guiding research questions, of the participants' backgrounds, of the processes of data collection, of the training and limitations of the coders, along with a strong attempt to make connections between the data and the conclusions evident.

Although the Berkenkotter, Huckin, and Ackerman (1988) study does not, case study reports often include the reactions of the participants to the study or to the researchers' conclusions. Because case studies tend to be exploratory, most end with implications for further study. Here researchers may identify significant variables that emerged during the research and suggest studies related to these, or the authors may suggest further general questions that their case study generated.

For example, Emig's (1971) study concludes with a section dedicated solely to the topic of implications for further research, in which she suggests several means by which this particular study could have been improved, as well as questions and ideas raised by this study which other researchers might like to address, such as: is there a correlation between a certain personality and a certain composing process profile (e.g. is there a positive correlation between ego strength and persistence in revising)?

Also included in Emig's study is a section dedicated to implications for teaching, which outlines the pedagogical ramifications of the study's findings for teachers currently involved in high school writing programs.

Sharan Merriam (1985) also offers several suggestions for alternative presentations of data:

  • Prepare specialized condensations for appropriate groups.
  • Replace narrative sections with a series of answers to open-ended questions.
  • Present "skimmer's" summaries at beginning of each section.
  • Incorporate headlines that encapsulate information from text.
  • Prepare analytic summaries with supporting data appendixes.
  • Present data in colorful and/or unique graphic representations.

Issues of Validity and Reliability

Once key variables have been identified, they can be analyzed. Reliability becomes a key concern at this stage, and many case study researchers go to great lengths to ensure that their interpretations of the data will be both reliable and valid. Because issues of validity and reliability are an important part of any study in the social sciences, it is important to identify some ways of dealing with results.

Multi-modal case study researchers often balance the results of their coding with data from interviews or writer's reflections upon their own work. Consequently, the researchers' conclusions become highly contextualized. For example, in a case study which looked at the time spent in different stages of the writing process, Berkenkotter concluded that her participant, Donald Murray, spent more time planning his essays than in other writing stages. The report of this case study is followed by Murray's reply, wherein he agrees with some of Berkenkotter's conclusions and disagrees with others.

As is the case with other research methodologies, issues of external validity, construct validity, and reliability need to be carefully considered.

Commentary on Case Studies

Researchers often debate the relative merits of particular methods, among them case study. In this section, we comment on two key issues. To read the commentaries, choose any of the items below:

Strengths and Weaknesses of Case Studies

Most case study advocates point out that case studies produce much more detailed information than what is available through a statistical analysis. Advocates will also hold that while statistical methods might be able to deal with situations where behavior is homogeneous and routine, case studies are needed to deal with creativity, innovation, and context. Detractors argue that case studies are difficult to generalize because of inherent subjectivity and because they are based on qualitative subjective data, generalizable only to a particular context.

Flexibility

The case study approach is a comparatively flexible method of scientific research. Because its project designs seem to emphasize exploration rather than prescription or prediction, researchers are comparatively freer to discover and address issues as they arise in their experiments. In addition, the looser format of case studies allows researchers to begin with broad questions and narrow their focus as their experiment progresses rather than attempt to predict every possible outcome before the experiment is conducted.

Emphasis on Context

By seeking to understand as much as possible about a single subject or small group of subjects, case studies specialize in "deep data," or "thick description"--information based on particular contexts that can give research results a more human face. This emphasis can help bridge the gap between abstract research and concrete practice by allowing researchers to compare their firsthand observations with the quantitative results obtained through other methods of research.

Inherent Subjectivity

"The case study has long been stereotyped as the weak sibling among social science methods," and is often criticized as being too subjective and even pseudo-scientific. Likewise, "investigators who do case studies are often regarded as having deviated from their academic disciplines, and their investigations as having insufficient precision (that is, quantification), objectivity and rigor" (Yin 1989). Opponents cite opportunities for subjectivity in the implementation, presentation, and evaluation of case study research. The approach relies on personal interpretation of data and inferences. Results may not be generalizable, are difficult to test for validity, and rarely offer a problem-solving prescription. Simply put, relying on one or a few subjects as a basis for cognitive extrapolations runs the risk of inferring too much from what might be circumstance.

High Investment

Case studies can involve learning more about the subjects being tested than most researchers would care to know--their educational background, emotional background, perceptions of themselves and their surroundings, their likes, dislikes, and so on. Because of its emphasis on "deep data," the case study is out of reach for many large-scale research projects which look at a subject pool in the tens of thousands. A budget request of $10,000 to examine 200 subjects sounds more efficient than a similar request to examine four subjects.

Ethical Considerations

Researchers conducting case studies should consider certain ethical issues. For example, many educational case studies are often financed by people who have, either directly or indirectly, power over both those being studied and those conducting the investigation (1985). This conflict of interests can hinder the credibility of the study.

The personal integrity, sensitivity, and possible prejudices and/or biases of the investigators need to be taken into consideration as well. Personal biases can creep into how the research is conducted, alternative research methods used, and the preparation of surveys and questionnaires.

A common complaint in case study research is that investigators change direction during the course of the study unaware that their original research design was inadequate for the revised investigation. Thus, the researchers leave unknown gaps and biases in the study. To avoid this, researchers should report preliminary findings so that the likelihood of bias will be reduced.

Concerns about Reliability, Validity, and Generalizability

Merriam (1985) offers several suggestions for how case study researchers might actively combat the popular attacks on the validity, reliability, and generalizability of case studies:

  • Prolong the Processes of Data Gathering on Site: This will help to insure the accuracy of the findings by providing the researcher with more concrete information upon which to formulate interpretations.
  • Employ the Process of "Triangulation": Use a variety of data sources as opposed to relying solely upon one avenue of observation. One example of such a data check would be what McClintock, Brannon, and Maynard (1985) refer to as a "case cluster method," that is, when a single unit within a larger case is randomly sampled, and that data treated quantitatively." For instance, in Emig's (1971) study, the case cluster method was employed, singling out the productivity of a single student named Lynn. This cluster profile included an advanced case history of the subject, specific examination and analysis of individual compositions and protocols, and extensive interview sessions. The seven remaining students were then compared with the case of Lynn, to ascertain if there are any shared, or unique dimensions to the composing process engaged in by these eight students.
  • Conduct Member Checks: Initiate and maintain an active corroboration on the interpretation of data between the researcher and those who provided the data. In other words, talk to your subjects.
  • Collect Referential Materials: Complement the file of materials from the actual site with additional document support. For example, Emig (1971) supports her initial propositions with historical accounts by writers such as T.S. Eliot, James Joyce, and D.H. Lawrence. Emig also cites examples of theoretical research done with regards to the creative process, as well as examples of empirical research dealing with the writing of adolescents. Specific attention is then given to the four stages description of the composing process delineated by Helmoltz, Wallas, and Cowley, as it serves as the focal point in this study.
  • Engage in Peer Consultation: Prior to composing the final draft of the report, researchers should consult with colleagues in order to establish validity through pooled judgment.

Although little can be done to combat challenges concerning the generalizability of case studies, "most writers suggest that qualitative research should be judged as credible and confirmable as opposed to valid and reliable" (Merriam 1985). Likewise, it has been argued that "rather than transplanting statistical, quantitative notions of generalizability and thus finding qualitative research inadequate, it makes more sense to develop an understanding of generalization that is congruent with the basic characteristics of qualitative inquiry" (1985). After all, criticizing the case study method for being ungeneralizable is comparable to criticizing a washing machine for not being able to tell the correct time. In other words, it is unjust to criticize a method for not being able to do something which it was never originally designed to do in the first place.

Annotated Bibliography

Armisted, C. (1984). How Useful are Case Studies. Training and Development Journal, 38 (2), 75-77.

This article looks at eight types of case studies, offers pros and cons of using case studies in the classroom, and gives suggestions for successfully writing and using case studies.

Bardovi-Harlig, K. (1997). Beyond Methods: Components of Second Language Teacher Education . New York: McGraw-Hill.

A compilation of various research essays which address issues of language teacher education. Essays included are: "Non-native reading research and theory" by Lee, "The case for Psycholinguistics" by VanPatten, and "Assessment and Second Language Teaching" by Gradman and Reed.

Bartlett, L. (1989). A Question of Good Judgment; Interpretation Theory and Qualitative Enquiry Address. 70th Annual Meeting of the American Educational Research Association. San Francisco.

Bartlett selected "quasi-historical" methodology, which focuses on the "truth" found in case records, as one that will provide "good judgments" in educational inquiry. He argues that although the method is not comprehensive, it can try to connect theory with practice.

Baydere, S. et. al. (1993). Multimedia conferencing as a tool for collaborative writing: a case study in Computer Supported Collaborative Writing. New York: Springer-Verlag.

The case study by Baydere et. al. is just one of the many essays in this book found in the series "Computer Supported Cooperative Work." Denley, Witefield and May explore similar issues in their essay, "A case study in task analysis for the design of a collaborative document production system."

Berkenkotter, C., Huckin, T., N., & Ackerman J. (1988). Conventions, Conversations, and the Writer: Case Study of a Student in a Rhetoric Ph.D. Program. Research in the Teaching of English, 22, 9-44.

The authors focused on how the writing of their subject, Nate or Ackerman, changed as he became more acquainted or familiar with his field's discourse community.

Berninger, V., W., and Gans, B., M. (1986). Language Profiles in Nonspeaking Individuals of Normal Intelligence with Severe Cerebral Palsy. Augmentative and Alternative Communication, 2, 45-50.

Argues that generalizations about language abilities in patients with severe cerebral palsy (CP) should be avoided. Standardized tests of different levels of processing oral language, of processing written language, and of producing written language were administered to 3 male participants (aged 9, 16, and 40 yrs).

Bockman, J., R., and Couture, B. (1984). The Case Method in Technical Communication: Theory and Models. Texas: Association of Teachers of Technical Writing.

Examines the study and teaching of technical writing, communication of technical information, and the case method in terms of those applications.

Boehrer, J. (1990). Teaching With Cases: Learning to Question. New Directions for Teaching and Learning, 42 41-57.

This article discusses the origins of the case method, looks at the question of what is a case, gives ideas about learning in case teaching, the purposes it can serve in the classroom, the ground rules for the case discussion, including the role of the question, and new directions for case teaching.

Bowman, W. R. (1993). Evaluating JTPA Programs for Economically Disadvantaged Adults: A Case Study of Utah and General Findings . Washington: National Commission for Employment Policy.

"To encourage state-level evaluations of JTPA, the Commission and the State of Utah co-sponsored this report on the effectiveness of JTPA Title II programs for adults in Utah. The technique used is non-experimental and the comparison group was selected from registrants with Utah's Employment Security. In a step-by-step approach, the report documents how non-experimental techniques can be applied and several specific technical issues can be addressed."

Boyce, A. (1993) The Case Study Approach for Pedagogists. Annual Meeting of the American Alliance for Health, Physical Education, Recreation and Dance. (Address). Washington DC.

This paper addresses how case studies 1) bridge the gap between teaching theory and application, 2) enable students to analyze problems and develop solutions for situations that will be encountered in the real world of teaching, and 3) helps students to evaluate the feasibility of alternatives and to understand the ramifications of a particular course of action.

Carson, J. (1993) The Case Study: Ideal Home of WAC Quantitative and Qualitative Data. Annual Meeting of the Conference on College Composition and Communication. (Address). San Diego.

"Increasingly, one of the most pressing questions for WAC advocates is how to keep [WAC] programs going in the face of numerous difficulties. Case histories offer the best chance for fashioning rhetorical arguments to keep WAC programs going because they offer the opportunity to provide a coherent narrative that contextualizes all documents and data, including what is generally considered scientific data. A case study of the WAC program, . . . at Robert Morris College in Pittsburgh demonstrates the advantages of this research method. Such studies are ideal homes for both naturalistic and positivistic data as well as both quantitative and qualitative information."

---. (1991). A Cognitive Process Theory of Writing. College Composition and Communication. 32. 365-87.

No abstract available.

Cromer, R. (1994) A Case Study of Dissociations Between Language and Cognition. Constraints on Language Acquisition: Studies of Atypical Children . Hillsdale: Lawrence Erlbaum Associates, 141-153.

Crossley, M. (1983) Case Study in Comparative and International Education: An Approach to Bridging the Theory-Practice Gap. Proceedings of the 11th Annual Conference of the Australian Comparative and International Education Society. Hamilton, NZ.

Case study research, as presented here, helps bridge the theory-practice gap in comparative and international research studies of education because it focuses on the practical, day-to-day context rather than on the national arena. The paper asserts that the case study method can be valuable at all levels of research, formation, and verification of theories in education.

Daillak, R., H., and Alkin, M., C. (1982). Qualitative Studies in Context: Reflections on the CSE Studies of Evaluation Use . California: EDRS

The report shows how the Center of the Study of Evaluation (CSE) applied qualitative techniques to a study of evaluation information use in local, Los Angeles schools. It critiques the effectiveness and the limitations of using case study, evaluation, field study, and user interview survey methodologies.

Davey, L. (1991). The Application of Case Study Evaluations. ERIC/TM Digest.

This article examines six types of case studies, the type of evaluation questions that can be answered, the functions served, some design features, and some pitfalls of the method.

Deutch, C. E. (1996). A course in research ethics for graduate students. College Teaching, 44, 2, 56-60.

This article describes a one-credit discussion course in research ethics for graduate students in biology. Case studies are focused on within the four parts of the course: 1) major issues, 2 )practical issues in scholarly work, 3) ownership of research results, and 4) training and personal decisions.

DeVoss, G. (1981). Ethics in Fieldwork Research. RIE 27p. (ERIC)

This article examines four of the ethical problems that can happen when conducting case study research: acquiring permission to do research, knowing when to stop digging, the pitfalls of doing collaborative research, and preserving the integrity of the participants.

Driscoll, A. (1985). Case Study of a Research Intervention: the University of Utah’s Collaborative Approach . San Francisco: Far West Library for Educational Research Development.

Paper presented at the annual meeting of the American Association of Colleges of Teacher Education, Denver, CO, March 1985. Offers information of in-service training, specifically case studies application.

Ellram, L. M. (1996). The Use of the Case Study Method in Logistics Research. Journal of Business Logistics, 17, 2, 93.

This article discusses the increased use of case study in business research, and the lack of understanding of when and how to use case study methodology in business.

Emig, J. (1971) The Composing Processes of Twelfth Graders . Urbana: NTCE.

This case study uses observation, tape recordings, writing samples, and school records to show that writing in reflexive and extensive situations caused different lengths of discourse and different clusterings of the components of the writing process.

Feagin, J. R. (1991). A Case For the Case Study . Chapel Hill: The University of North Carolina Press.

This book discusses the nature, characteristics, and basic methodological issues of the case study as a research method.

Feldman, H., Holland, A., & Keefe, K. (1989) Language Abilities after Left Hemisphere Brain Injury: A Case Study of Twins. Topics in Early Childhood Special Education, 9, 32-47.

"Describes the language abilities of 2 twin pairs in which 1 twin (the experimental) suffered brain injury to the left cerebral hemisphere around the time of birth and1 twin (the control) did not. One pair of twins was initially assessed at age 23 mo. and the other at about 30 mo.; they were subsequently evaluated in their homes 3 times at about 6-mo intervals."

Fidel, R. (1984). The Case Study Method: A Case Study. Library and Information Science Research, 6.

The article describes the use of case study methodology to systematically develop a model of online searching behavior in which study design is flexible, subject manner determines data gathering and analyses, and procedures adapt to the study's progressive change.

Flower, L., & Hayes, J. R. (1984). Images, Plans and Prose: The Representation of Meaning in Writing. Written Communication, 1, 120-160.

Explores the ways in which writers actually use different forms of knowing to create prose.

Frey, L. R. (1992). Interpreting Communication Research: A Case Study Approach Englewood Cliffs, N.J.: Prentice Hall.

The book discusses research methodologies in the Communication field. It focuses on how case studies bridge the gap between communication research, theory, and practice.

Gilbert, V. K. (1981). The Case Study as a Research Methodology: Difficulties and Advantages of Integrating the Positivistic, Phenomenological and Grounded Theory Approaches . The Annual Meeting of the Canadian Association for the Study of Educational Administration. (Address) Halifax, NS, Can.

This study on an innovative secondary school in England shows how a "low-profile" participant-observer case study was crucial to the initial observation, the testing of hypotheses, the interpretive approach, and the grounded theory.

Gilgun, J. F. (1994). A Case for Case Studies in Social Work Research. Social Work, 39, 4, 371-381.

This article defines case study research, presents guidelines for evaluation of case studies, and shows the relevance of case studies to social work research. It also looks at issues such as evaluation and interpretations of case studies.

Glennan, S. L., Sharp-Bittner, M. A. & Tullos, D. C. (1991). Augmentative and Alternative Communication Training with a Nonspeaking Adult: Lessons from MH. Augmentative and Alternative Communication, 7, 240-7.

"A response-guided case study documented changes in a nonspeaking 36-yr-old man's ability to communicate using 3 trained augmentative communication modes. . . . Data were collected in videotaped interaction sessions between the nonspeaking adult and a series of adult speaking."

Graves, D. (1981). An Examination of the Writing Processes of Seven Year Old Children. Research in the Teaching of English, 15, 113-134.

Hamel, J. (1993). Case Study Methods . Newbury Park: Sage. .

"In a most economical fashion, Hamel provides a practical guide for producing theoretically sharp and empirically sound sociological case studies. A central idea put forth by Hamel is that case studies must "locate the global in the local" thus making the careful selection of the research site the most critical decision in the analytic process."

Karthigesu, R. (1986, July). Television as a Tool for Nation-Building in the Third World: A Post-Colonial Pattern, Using Malaysia as a Case-Study. International Television Studies Conference. (Address). London, 10-12.

"The extent to which Television Malaysia, as a national mass media organization, has been able to play a role in nation building in the post-colonial period is . . . studied in two parts: how the choice of a model of nation building determines the character of the organization; and how the character of the organization influences the output of the organization."

Kenny, R. (1984). Making the Case for the Case Study. Journal of Curriculum Studies, 16, (1), 37-51.

The article looks at how and why the case study is justified as a viable and valuable approach to educational research and program evaluation.

Knirk, F. (1991). Case Materials: Research and Practice. Performance Improvement Quarterly, 4 (1 ), 73-81.

The article addresses the effectiveness of case studies, subject areas where case studies are commonly used, recent examples of their use, and case study design considerations.

Klos, D. (1976). Students as Case Writers. Teaching of Psychology, 3.2, 63-66.

This article reviews a course in which students gather data for an original case study of another person. The task requires the students to design the study, collect the data, write the narrative, and interpret the findings.

Leftwich, A. (1981). The Politics of Case Study: Problems of Innovation in University Education. Higher Education Review, 13.2, 38-64.

The article discusses the use of case studies as a teaching method. Emphasis is on the instructional materials, interdisciplinarity, and the complex relationships within the university that help or hinder the method.

Mabrito, M. (1991, Oct.). Electronic Mail as a Vehicle for Peer Response: Conversations of High and Low Apprehensive Writers. Written Communication, 509-32.

McCarthy, S., J. (1955). The Influence of Classroom Discourse on Student Texts: The Case of Ella . East Lansing: Institute for Research on Teaching.

A look at how students of color become marginalized within traditional classroom discourse. The essay follows the struggles of one black student: Ella.

Matsuhashi, A., ed. (1987). Writing in Real Time: Modeling Production Processes Norwood, NJ: Ablex Publishing Corporation.

Investigates how writers plan to produce discourse for different purposes to report, to generalize, and to persuade, as well as how writers plan for sentence level units of language. To learn about planning, an observational measure of pause time was used" (ERIC).

Merriam, S. B. (1985). The Case Study in Educational Research: A Review of Selected Literature. Journal of Educational Thought, 19.3, 204-17.

The article examines the characteristics of, philosophical assumptions underlying the case study, the mechanics of conducting a case study, and the concerns about the reliability, validity, and generalizability of the method.

---. (1988). Case Study Research in Education: A Qualitative Approach San Francisco: Jossey Bass.

Merry, S. E., & Milner, N. eds. (1993). The Possibility of Popular Justice: A Case Study of Community Mediation in the United States . Ann Arbor: U of Michigan.

". . . this volume presents a case study of one experiment in popular justice, the San Francisco Community Boards. This program has made an explicit claim to create an alternative justice, or new justice, in the midst of a society ordered by state law. The contributors to this volume explore the history and experience of the program and compare it to other versions of popular justice in the United States, Europe, and the Third World."

Merseth, K. K. (1991). The Case for Cases in Teacher Education. RIE. 42p. (ERIC).

This monograph argues that the case method of instruction offers unique potential for revitalizing the field of teacher education.

Michaels, S. (1987). Text and Context: A New Approach to the Study of Classroom Writing. Discourse Processes, 10, 321-346.

"This paper argues for and illustrates an approach to the study of writing that integrates ethnographic analysis of classroom interaction with linguistic analysis of written texts and teacher/student conversational exchanges. The approach is illustrated through a case study of writing in a single sixth grade classroom during a single writing assignment."

Milburn, G. (1995). Deciphering a Code or Unraveling a Riddle: A Case Study in the Application of a Humanistic Metaphor to the Reporting of Social Studies Teaching. Theory and Research in Education, 13.

This citation serves as an example of how case studies document learning procedures in a senior-level economics course.

Milley, J. E. (1979). An Investigation of Case Study as an Approach to Program Evaluation. 19th Annual Forum of the Association for Institutional Research. (Address). San Diego.

The case study method merged a narrative report focusing on the evaluator as participant-observer with document review, interview, content analysis, attitude questionnaire survey, and sociogram analysis. Milley argues that case study program evaluation has great potential for widespread use.

Minnis, J. R. (1985, Sept.). Ethnography, Case Study, Grounded Theory, and Distance Education Research. Distance Education, 6.2.

This article describes and defines the strengths and weaknesses of ethnography, case study, and grounded theory.

Nunan, D. (1992). Collaborative language learning and teaching . New York: Cambridge University Press.

Included in this series of essays is Peter Sturman’s "Team Teaching: a case study from Japan" and David Nunan’s own "Toward a collaborative approach to curriculum development: a case study."

Nystrand, M., ed. (1982). What Writers Know: The Language, Process, and Structure of Written Discourse . New York: Academic Press.

Owenby, P. H. (1992). Making Case Studies Come Alive. Training, 29, (1), 43-46. (ERIC)

This article provides tips for writing more effective case studies.

---. (1981). Pausing and Planning: The Tempo of Writer Discourse Production. Research in the Teaching of English, 15 (2),113-34.

Perl, S. (1979). The Composing Processes of Unskilled College Writers. Research in the Teaching of English, 13, 317-336.

"Summarizes a study of five unskilled college writers, focusing especially on one of the five, and discusses the findings in light of current pedagogical practice and research design."

Pilcher J. and A. Coffey. eds. (1996). Gender and Qualitative Research . Brookfield: Aldershot, Hants, England.

This book provides a series of essays which look at gender identity research, qualitative research and applications of case study to questions of gendered pedagogy.

Pirie, B. S. (1993). The Case of Morty: A Four Year Study. Gifted Education International, 9 (2), 105-109.

This case study describes a boy from kindergarten through third grade with above average intelligence but difficulty in learning to read, write, and spell.

Popkewitz, T. (1993). Changing Patterns of Power: Social Regulation and Teacher Education Reform. Albany: SUNY Press.

Popkewitz edits this series of essays that address case studies on educational change and the training of teachers. The essays vary in terms of discipline and scope. Also, several authors include case studies of educational practices in countries other than the United States.

---. (1984). The Predrafting Processes of Four High- and Four Low Apprehensive Writers. Research in the Teaching of English, 18, (1), 45-64.

Rasmussen, P. (1985, March) A Case Study on the Evaluation of Research at the Technical University of Denmark. International Journal of Institutional Management in Higher Education, 9 (1).

This is an example of a case study methodology used to evaluate the chemistry and chemical engineering departments at the University of Denmark.

Roth, K. J. (1986). Curriculum Materials, Teacher Talk, and Student Learning: Case Studies in Fifth-Grade Science Teaching . East Lansing: Institute for Research on Teaching.

Roth offers case studies on elementary teachers, elementary school teaching, science studies and teaching, and verbal learning.

Selfe, C. L. (1985). An Apprehensive Writer Composes. When a Writer Can't Write: Studies in Writer's Block and Other Composing-Process Problems . (pp. 83-95). Ed. Mike Rose. NMY: Guilford.

Smith-Lewis, M., R. and Ford, A. (1987). A User's Perspective on Augmentative Communication. Augmentative and Alternative Communication, 3, 12-7.

"During a series of in-depth interviews, a 25-yr-old woman with cerebral palsy who utilized augmentative communication reflected on the effectiveness of the devices designed for her during her school career."

St. Pierre, R., G. (1980, April). Follow Through: A Case Study in Metaevaluation Research . 64th Annual Meeting of the American Educational Research Association. (Address).

The three approaches to metaevaluation are evaluation of primary evaluations, integrative meta-analysis with combined primary evaluation results, and re-analysis of the raw data from a primary evaluation.

Stahler, T., M. (1996, Feb.) Early Field Experiences: A Model That Worked. ERIC.

"This case study of a field and theory class examines a model designed to provide meaningful field experiences for preservice teachers while remaining consistent with the instructor's beliefs about the role of teacher education in preparing teachers for the classroom."

Stake, R. E. (1995). The Art of Case Study Research. Thousand Oaks: Sage Publications.

This book examines case study research in education and case study methodology.

Stiegelbauer, S. (1984) Community, Context, and Co-curriculum: Situational Factors Influencing School Improvements in a Study of High Schools. Presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Discussion of several case studies: one looking at high school environments, another examining educational innovations.

Stolovitch, H. (1990). Case Study Method. Performance And Instruction, 29, (9), 35-37.

This article describes the case study method as a form of simulation and presents guidelines for their use in professional training situations.

Thaller, E. (1994). Bibliography for the Case Method: Using Case Studies in Teacher Education. RIE. 37 p.

This bibliography presents approximately 450 citations on the use of case studies in teacher education from 1921-1993.

Thrane, T. (1986). On Delimiting the Senses of Near-Synonyms in Historical Semantics: A Case Study of Adjectives of 'Moral Sufficiency' in the Old English Andreas. Linguistics Across Historical and Geographical Boundaries: In Honor of Jacek Fisiak on the Occasion of his Fiftieth Birthday . Berlin: Mouton de Gruyter.

United Nations. (1975). Food and Agriculture Organization. Report on the FAO/UNFPA Seminar on Methodology, Research and Country: Case Studies on Population, Employment and Productivity . Rome: United Nations.

This example case study shows how the methodology can be used in a demographic and psychographic evaluation. At the same time, it discusses the formation and instigation of the case study methodology itself.

Van Vugt, J. P., ed. (1994). Aids Prevention and Services: Community Based Research . Westport: Bergin and Garvey.

"This volume has been five years in the making. In the process, some of the policy applications called for have met with limited success, such as free needle exchange programs in a limited number of American cities, providing condoms to prison inmates, and advertisements that depict same-sex couples. Rather than dating our chapters that deal with such subjects, such policy applications are verifications of the type of research demonstrated here. Furthermore, they indicate the critical need to continue community based research in the various communities threatened by acquired immuno-deficiency syndrome (AIDS) . . . "

Welch, W., ed. (1981, May). Case Study Methodology in Educational Evaluation. Proceedings of the Minnesota Evaluation Conference. Minnesota. (Address).

The four papers in these proceedings provide a comprehensive picture of the rationale, methodology, strengths, and limitations of case studies.

Williams, G. (1987). The Case Method: An Approach to Teaching and Learning in Educational Administration. RIE, 31p.

This paper examines the viability of the case method as a teaching and learning strategy in instructional systems geared toward the training of personnel of the administration of various aspects of educational systems.

Yin, R. K. (1993). Advancing Rigorous Methodologies: A Review of 'Towards Rigor in Reviews of Multivocal Literatures.' Review of Educational Research, 61, (3).

"R. T. Ogawa and B. Malen's article does not meet its own recommended standards for rigorous testing and presentation of its own conclusions. Use of the exploratory case study to analyze multivocal literatures is not supported, and the claim of grounded theory to analyze multivocal literatures may be stronger."

---. (1989). Case Study Research: Design and Methods. London: Sage Publications Inc.

This book discusses in great detail, the entire design process of the case study, including entire chapters on collecting evidence, analyzing evidence, composing the case study report, and designing single and multiple case studies.

Related Links

Consider the following list of related Web sites for more information on the topic of case study research. Note: although many of the links cover the general category of qualitative research, all have sections that address issues of case studies.

  • Sage Publications on Qualitative Methodology: Search here for a comprehensive list of new books being published about "Qualitative Methodology" http://www.sagepub.co.uk/
  • The International Journal of Qualitative Studies in Education: An on-line journal "to enhance the theory and practice of qualitative research in education." On-line submissions are welcome. http://www.tandf.co.uk/journals/tf/09518398.html
  • Qualitative Research Resources on the Internet: From syllabi to home pages to bibliographies. All links relate somehow to qualitative research. http://www.nova.edu/ssss/QR/qualres.html

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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.

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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.

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Psychological Research

Descriptive research, learning objectives.

  • Differentiate between descriptive, experimental, and correlational research
  • Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

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 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. Table 1 displays a quick overview of the characteristics of each research design.

Table 1. Characteristics of Descriptive, Experimental, and Correlational Research

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 this allows for 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 this 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 in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This 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.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

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 hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses 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).

A photograph shows two police cars driving, one with its lights flashing.

Figure 1 . Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)

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. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

(a) A photograph shows Jane Goodall speaking from a lectern. (b) A photograph shows a chimpanzee’s face.

Figure 2 . (a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)

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 or animals 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. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

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. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

A sample online survey reads, “Dear visitor, your opinion is important to us. We would like to invite you to participate in a short survey to gather your opinions and feedback on your news consumption habits. The survey will take approximately 10-15 minutes. Simply click the “Yes” button below to launch the survey. Would you like to participate?” Two buttons are labeled “yes” and “no.”

Figure 3 . 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 of the survey in comparison 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 don’t 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 US 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

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

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

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

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

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

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

Free eBook: 2024 global market research trends report

What is descriptive research?

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

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

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

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

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

Descriptive research design

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

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

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

Descriptive research characteristics

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

Here are a few of the things to consider:

Measure data trends with statistical outcomes

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

Descriptive research design is ideal for further research

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

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

Uncontrolled variables

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

It’s carried out in a natural environment

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

Data can be gathered using survey questions or online surveys.

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

Descriptive research allows for cross sectional study

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

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

Descriptive research advantages

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

Cost effectiveness

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

Multiple types of data can be collected

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

Descriptive research disadvantages

Potential reliability issues.

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

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

Potential limitations

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

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

Descriptive research methods

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

Use case studies

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

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

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

Use observational methods

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

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

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

Use survey research

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

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

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

Descriptive research examples

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

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

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

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

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

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

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

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

Where Qualtrics helps with descriptive research

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

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

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

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

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

Table of Contents

What Is Descriptive Research Design?

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

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

Importance of Descriptive Research in Scientific Studies

1. understanding of a population or phenomenon.

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

2. Baseline Information

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

3. Informative Data

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

4. Sampling Validation

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

5. Cost Effective

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

6. Easy to Replicate

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

Key Characteristics of Descriptive Research Design

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

2. Participants and Sampling

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

3. Data Collection Techniques

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

4. Data Analysis

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

5. Focus on Description

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

6. Non-Experimental

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

When Can a Researcher Conduct Descriptive Research?

A researcher can conduct descriptive research in the following situations:

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

Types of Descriptive Research Design

1. survey research.

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

2. Observational Research

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

3. Case Study Research

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

4. Focus Group Research

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

5. Ethnographic Research

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

Advantages of Descriptive Research Design

1. provides a comprehensive understanding.

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

2. Non-invasive

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

3. Flexibility

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

4. Cost-effective

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

5. Easy to Replicate

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

6. Informs Future Research

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

Disadvantages of Descriptive Research Design

1. limited scope.

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

2. Dependence on Existing Data

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

3. Lack of Control

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

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

5. Lack of Generalizability

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

6. Lack of Depth

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

7. Time-consuming

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

7 Ways to Avoid Common Flaws While Designing Descriptive Research

are case studies descriptive research

1. Clearly define the research question

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

2. Choose the appropriate research design

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

3. Select a representative sample

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

4. Use valid and reliable data collection methods

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

5. Minimize bias

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

6. Ensure adequate sample size

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

7. Use appropriate data analysis techniques

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

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

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Child Care and Early Education Research Connections

Descriptive research studies.

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

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

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

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

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

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

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

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

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

Limitations:

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

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

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

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

  • Descriptive Research Designs: Types, Examples & Methods

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

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

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

What is Descriptive Research?

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

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

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

What Are The Types of Descriptive Research?

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

  • Descriptive-survey

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

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

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

  • Descriptive-normative survey

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

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

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

  • Descriptive-status

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

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

  • Descriptive-analysis

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

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

  • Descriptive classification

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

  • Descriptive-comparative

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

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

  • Correlative Survey

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

Examples of Descriptive Research

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

  • Comparing Student Performance:

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

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

  • Scientific Classification

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

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

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

  • Human Behavior

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

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

What are the Characteristics of Descriptive Research?  

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

  • Quantitativeness

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

  • Qualitativeness

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

  • Uncontrolled variables

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

  • The basis for further research

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

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

Why Use Descriptive Research Design?  

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

  • Define subject characteristics :

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

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

  • Measure Data Trends

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

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

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

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

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

  • Validate existing conditions

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

  • Conducted Overtime

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

What are the Disadvantages of Descriptive Research?  

  • Response and Non-response Bias

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

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

What are the Data Collection Methods in Descriptive Research?  

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

1. Observational Method

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

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

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

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

2. Case Study Method

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

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

3. Survey Research

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

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

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

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

What Are The Differences Between Descriptive and Correlational Research?  

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

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

Correlational research may be used in 2 situations;

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

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

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

  • Definitions :

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

  • Characteristics :

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

  • Predictions :

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

Descriptive Research vs. Causal Research

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

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

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

Descriptive Research vs. Analytical Research

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

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

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

Descriptive Research vs. Exploratory Research

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

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

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

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

Descriptive Research vs. Experimental Research

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

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

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

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

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

Descriptive Research vs. Explanatory Research

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

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

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

Descriptive Research vs. Inferential Research

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

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

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

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

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

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

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

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

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  • What is descriptive research?

Last updated

5 February 2023

Reviewed by

Cathy Heath

Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia.

Read on to understand the characteristics of descriptive research and explore its underlying techniques, processes, and procedures.

Analyze your descriptive research

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Descriptive research is an exploratory research method. It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.

As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses . This can be reported using surveys , observational studies, and case studies. You can use both quantitative and qualitative methods to compile the data.

Besides making observations and then comparing and analyzing them, descriptive studies often develop knowledge concepts and provide solutions to critical issues. It always aims to answer how the event occurred, when it occurred, where it occurred, and what the problem or phenomenon is.

  • Characteristics of descriptive research

The following are some of the characteristics of descriptive research:

Quantitativeness

Descriptive research can be quantitative as it gathers quantifiable data to statistically analyze a population sample. These numbers can show patterns, connections, and trends over time and can be discovered using surveys, polls, and experiments.

Qualitativeness

Descriptive research can also be qualitative. It gives meaning and context to the numbers supplied by quantitative descriptive research .

Researchers can use tools like interviews, focus groups, and ethnographic studies to illustrate why things are what they are and help characterize the research problem. This is because it’s more explanatory than exploratory or experimental research.

Uncontrolled variables

Descriptive research differs from experimental research in that researchers cannot manipulate the variables. They are recognized, scrutinized, and quantified instead. This is one of its most prominent features.

Cross-sectional studies

Descriptive research is a cross-sectional study because it examines several areas of the same group. It involves obtaining data on multiple variables at the personal level during a certain period. It’s helpful when trying to understand a larger community’s habits or preferences.

Carried out in a natural environment

Descriptive studies are usually carried out in the participants’ everyday environment, which allows researchers to avoid influencing responders by collecting data in a natural setting. You can use online surveys or survey questions to collect data or observe.

Basis for further research

You can further dissect descriptive research’s outcomes and use them for different types of investigation. The outcomes also serve as a foundation for subsequent investigations and can guide future studies. For example, you can use the data obtained in descriptive research to help determine future research designs.

  • Descriptive research methods

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey.

You can use surveys to gather data in descriptive research. This involves gathering information from many people using a questionnaire and interview .

Surveys remain the dominant research tool for descriptive research design. Researchers can conduct various investigations and collect multiple types of data (quantitative and qualitative) using surveys with diverse designs.

You can conduct surveys over the phone, online, or in person. Your survey might be a brief interview or conversation with a set of prepared questions intended to obtain quick information from the primary source.

Observation

This descriptive research method involves observing and gathering data on a population or phenomena without manipulating variables. It is employed in psychology, market research , and other social science studies to track and understand human behavior.

Observation is an essential component of descriptive research. It entails gathering data and analyzing it to see whether there is a relationship between the two variables in the study. This strategy usually allows for both qualitative and quantitative data analysis.

Case studies

A case study can outline a specific topic’s traits. The topic might be a person, group, event, or organization.

It involves using a subset of a larger group as a sample to characterize the features of that larger group.

You can generalize knowledge gained from studying a case study to benefit a broader audience.

This approach entails carefully examining a particular group, person, or event over time. You can learn something new about the study topic by using a small group to better understand the dynamics of the entire group.

  • Types of descriptive research

There are several types of descriptive study. The most well-known include cross-sectional studies, census surveys, sample surveys, case reports, and comparison studies.

Case reports and case series

In the healthcare and medical fields, a case report is used to explain a patient’s circumstances when suffering from an uncommon illness or displaying certain symptoms. Case reports and case series are both collections of related cases. They have aided the advancement of medical knowledge on countless occasions.

The normative component is an addition to the descriptive survey. In the descriptive–normative survey, you compare the study’s results to the norm.

Descriptive survey

This descriptive type of research employs surveys to collect information on various topics. This data aims to determine the degree to which certain conditions may be attained.

You can extrapolate or generalize the information you obtain from sample surveys to the larger group being researched.

Correlative survey

Correlative surveys help establish if there is a positive, negative, or neutral connection between two variables.

Performing census surveys involves gathering relevant data on several aspects of a given population. These units include individuals, families, organizations, objects, characteristics, and properties.

During descriptive research, you gather different degrees of interest over time from a specific population. Cross-sectional studies provide a glimpse of a phenomenon’s prevalence and features in a population. There are no ethical challenges with them and they are quite simple and inexpensive to carry out.

Comparative studies

These surveys compare the two subjects’ conditions or characteristics. The subjects may include research variables, organizations, plans, and people.

Comparison points, assumption of similarities, and criteria of comparison are three important variables that affect how well and accurately comparative studies are conducted.

For instance, descriptive research can help determine how many CEOs hold a bachelor’s degree and what proportion of low-income households receive government help.

  • Pros and cons

The primary advantage of descriptive research designs is that researchers can create a reliable and beneficial database for additional study. To conduct any inquiry, you need access to reliable information sources that can give you a firm understanding of a situation.

Quantitative studies are time- and resource-intensive, so knowing the hypotheses viable for testing is crucial. The basic overview of descriptive research provides helpful hints as to which variables are worth quantitatively examining. This is why it’s employed as a precursor to quantitative research designs.

Some experts view this research as untrustworthy and unscientific. However, there is no way to assess the findings because you don’t manipulate any variables statistically.

Cause-and-effect correlations also can’t be established through descriptive investigations. Additionally, observational study findings cannot be replicated, which prevents a review of the findings and their replication.

The absence of statistical and in-depth analysis and the rather superficial character of the investigative procedure are drawbacks of this research approach.

  • Descriptive research examples and applications

Several descriptive research examples are emphasized based on their types, purposes, and applications. Research questions often begin with “What is …” These studies help find solutions to practical issues in social science, physical science, and education.

Here are some examples and applications of descriptive research:

Determining consumer perception and behavior

Organizations use descriptive research designs to determine how various demographic groups react to a certain product or service.

For example, a business looking to sell to its target market should research the market’s behavior first. When researching human behavior in response to a cause or event, the researcher pays attention to the traits, actions, and responses before drawing a conclusion.

Scientific classification

Scientific descriptive research enables the classification of organisms and their traits and constituents.

Measuring data trends

A descriptive study design’s statistical capabilities allow researchers to track data trends over time. It’s frequently used to determine the study target’s current circumstances and underlying patterns.

Conduct comparison

Organizations can use a descriptive research approach to learn how various demographics react to a certain product or service. For example, you can study how the target market responds to a competitor’s product and use that information to infer their behavior.

  • Bottom line

A descriptive research design is suitable for exploring certain topics and serving as a prelude to larger quantitative investigations. It provides a comprehensive understanding of the “what” of the group or thing you’re investigating.

This research type acts as the cornerstone of other research methodologies . It is distinctive because it can use quantitative and qualitative research approaches at the same time.

What is descriptive research design?

Descriptive research design aims to systematically obtain information to describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem rather than the why.

How does descriptive research compare to qualitative research?

Despite certain parallels, descriptive research concentrates on describing phenomena, while qualitative research aims to understand people better.

How do you analyze descriptive research data?

Data analysis involves using various methodologies, enabling the researcher to evaluate and provide results regarding validity and reliability.

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are case studies descriptive research

Home Market Research

Descriptive Research: Definition, Characteristics, Methods + Examples

Descriptive Research

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

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

What is descriptive research?

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

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

Characteristics of descriptive research

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

Some distinctive characteristics of descriptive research are:

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

Applications of descriptive research with examples

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

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

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

Advantages of descriptive research

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

Descriptive research methods

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

Observational method

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

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

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

Case study method

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

Survey research

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

Examples of descriptive research

Some examples of descriptive research are:

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

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

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

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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.

DifferenceSilo

difference between case study and vs descriptive approach to research

Introduction

The distinction between a case study and a descriptive approach to research is one of the most fundamental and important distinctions in the social sciences. Case studies and descriptive research both have their own strengths and weaknesses, and understanding the difference between them is essential for any researcher looking to make the most of their research. In this article, we will explore the differences between case studies and descriptive research, as well as provide a list of frequently asked questions about the two approaches.

What is a Case Study?

A case study is an empirical research method used to investigate a particular phenomenon within its real-life context. Case studies typically focus on a single individual, group, event, or organization, and involve an in-depth exploration of the phenomenon using multiple sources of evidence. Case studies are often used to explore complex social, psychological, and organizational phenomena, as they allow researchers to gain an in-depth understanding of the phenomenon.

What is Descriptive Research?

Descriptive research is an empirical research method used to describe a phenomenon or population. It is used to collect data about a population or phenomenon and to answer questions about it. Descriptive research typically involves collecting data through surveys, interviews, or experiments, and then analyzing the data to understand the population or phenomenon.

Differences Between Case Studies and Descriptive Research

The main difference between case studies and descriptive research is the type of data collected and analyzed. Case studies collect qualitative data through interviews and observations, while descriptive research typically collects quantitative data through surveys and experiments. Additionally, case studies are typically focused on a single individual, group, event, or organization, while descriptive research is focused on a population or phenomenon.

1. What is the difference between a case study and a descriptive approach to research? The main difference between a case study and a descriptive approach to research is the type of data collected and analyzed. Case studies collect qualitative data through interviews and observations, while descriptive research typically collects quantitative data through surveys and experiments. Additionally, case studies are typically focused on a single individual, group, event, or organization, while descriptive research is focused on a population or phenomenon.

2. What is the purpose of a case study? The purpose of a case study is to gain an in-depth understanding of a particular phenomenon within its real-life context. Case studies are often used to explore complex social, psychological, and organizational phenomena, as they allow researchers to gain an in-depth understanding of the phenomenon.

3. What is the purpose of descriptive research? The purpose of descriptive research is to collect data about a population or phenomenon and to answer questions about it. Descriptive research typically involves collecting data through surveys, interviews, or experiments, and then analyzing the data to understand the population or phenomenon.

4. What types of data do case studies collect? Case studies typically collect qualitative data through interviews and observations. Qualitative data is often more detailed and can provide a deeper understanding of the phenomenon being studied.

5. What types of data do descriptive research collect? Descriptive research typically collects quantitative data through surveys and experiments. Quantitative data is often more objective and can provide a broader understanding of the population or phenomenon being studied.

6. How can case studies be used? Case studies can be used to explore complex social, psychological, and organizational phenomena, as they allow researchers to gain an in-depth understanding of the phenomenon. Additionally, case studies can be used to evaluate the effectiveness of a program or policy, or to develop new theories or models.

Understanding the differences between case studies and descriptive research is essential for any researcher looking to make the most of their research. Case studies and descriptive research both have their own strengths and weaknesses, and understanding the difference between them is essential for any researcher looking to make the most of their research. By understanding the differences between these two approaches, researchers can make an informed decision about which approach best suits their research needs.

  • Open access
  • Published: 02 January 2024

Outcomes of the combined lifestyle intervention CooL during COVID-19: a descriptive case series study

  • Ester Janssen 1   na1 ,
  • Nicole Philippens 1 ,
  • Stef Kremers 1 &
  • Rik Crutzen 2  

BMC Public Health volume  24 , Article number:  40 ( 2024 ) Cite this article

Metrics details

The main objective of this nationwide study was to investigate changes in outcomes between baseline and eight months of participation regarding anthropometrics, control and support, physical activity, diet attentiveness, perceived fitness, sleep, and stress of participants in Coaching on Lifestyle (CooL), a Combined Lifestyle Intervention (CLI). Since the study took place when the COVID-19 pandemic emerged, we defined a subobjective, i.e., to address changes in intervention outcomes over time while participants were exposed to pandemic-related restrictions and uncertainties.

Data were collected from November 2018 until October 2021 at different locations across the Netherlands from 1824 participating adults, meeting the CLI inclusion criteria. We collected a broad set of data on anthropometrics (weight, body mass index (BMI), waist circumference), control and support (self-mastery, social support), physical activity (sedentary time on least/most active days, physical active minutes), diet attentiveness (attentiveness to meal composition, awareness to amounts of food and attentiveness to consuming), alcohol consumption, smoking, perceived fitness (perceived health, fitness when waking, fitness during daytime, impact daily stress), sleep and stress.

All outcomes showed improvements after eight months compared to baseline except for social support and smoking. Large effect sizes were found on weight (0.57), waist circumference (0.50) and perceived health (0.50). Behaviour patterns showed small to large effect sizes, with the largest effect sizes on diet attentiveness (i.e., attentiveness to meal composition (0.43), awareness to amounts of food (0.58) and attentiveness to consuming (0.39)). The outcomes of participants pre COVID-19 versus during COVID-19 showed differences on self-mastery (p = 0.01), sedentary time (all underlying constructs p < 0.02), perceived fitness (all underlying constructs p < 0.02) and stress (p < 0.01).

The results show that small changes in multiple behaviours go along with a large positive change in perceived health and health-related outcomes in line with the lifestyle coaching principles. In addition, participating in CooL may have protected against engaging in unhealthier behaviour during the pandemic.

Trial registration

As the CLI is considered usual health care that does not fall within the scope of the Dutch Medical Research Involving Human Subjects Act, this study was exempt from trial registration.

Peer Review reports

In 2021, 50% of Dutch people aged 18 and older, were overweight and approximately 14% were obese [ 1 ]. Obesity is considered a disease according to the World Health Organisation [ 2 ] and the Dutch Health council [ 3 ]. Furthermore, obesity is associated with an increased risk for many other diseases, such as diabetes mellitus type 2, cardiovascular disease, various cancers [ 4 , 5 , 6 ], mental health problems (e.g., depression) [ 7 ] and a diminished quality of life [ 8 ]. Consensus has been reached internationally [ 9 ] on the importance of an integrated approach to target overweight and obesity, including limited energy intake, healthy food choices and regular physical activity. The Dutch national guidelines added stress management and sleep as essential elements to tackle overweight and obesity [ 10 ].

As of January 2019, Combined Lifestyle Interventions (CLIs) are part of basic health insurance in the Netherlands. A CLI is an intervention for people with overweight or obesity, stimulating weight reduction by promoting sustained healthier behaviour. In the intervention, participants are coached towards a healthier lifestyle. The CLIs exist of a combination of group and individual sessions and cover at least the topics of healthy diet, physical activity and behavioural change. Based on the Dutch national guidelines on the treatment of obesity and the relationship between stress and obesity and sleep and obesity, both lifestyle themes are also considered an essential part of the CLI [ 11 , 12 ].

The inclusion criteria for CLIs in the Netherlands are: [ 1 ] being 18 or older; (2a) having a Body Mass Index (BMI) between 25 and 30 kg/m2 in combination with a waist circumference over 88 cm for women or over 102 cm for men, or with comorbidity (increased risk of) diabetes or cardiovascular disease, osteoarthritis or sleep apnea), or (2b) having a BMI > 30 kg/m2 regardless of waist size or comorbidity; and [ 3 ] being sufficiently motivated to complete the two-year intervention as judged by the referrer (e.g., the general practitioner or practice nurse) and the CLI-coach.

The Coaching on Lifestyle intervention (CooL) is one of six CLIs that are approved by the Dutch Institute for Public Health and Environment (in Dutch: RIVM) for being effective in facilitating weight reduction. The intervention has two phases: an intensive behavioural change phase of eight months, followed by a less-intensive 16-month behavioural maintenance phase summing up to a total duration of two years. Baseline measurements are done during intake, followed by measurements after the behavioural change phase (8 months) and after the behavioural maintenance phase (24 months). So far, research on the CLI has been done on Slimmer [ 13 ] and Beweegkuur [ 14 ] and on CooL in a regional setting: the CooL-pilot and the healthyLIFE study [ 15 , 16 ]. All CLI’s are showing comparable weight loss as well as additional benefits in positive health [ 16 ], metabolic risk factors [ 13 , 14 ] and/or health related behaviour [ 13 , 14 , 15 , 16 ]. The main objective of the present nationwide study is to look at the changes in outcomes of participants in the behavioural change phase of the CooL-intervention on the topics of anthropometrics (weight, BMI, waist circumference), control and support (self-mastery, social support), physical activity (sedentary time on least/most active days, physical active minutes), diet attentiveness (attentiveness to meal composition, awareness to amounts of food and attentiveness to consuming), alcohol consumption, smoking, perceived fitness (perceived health, fitness when waking, fitness during daytime, impact daily stress), sleep and stress (see Table  1 ).

The COVID-19 pandemic entered the Netherlands in February 2020, resulting in stringent COVID-19 measures that came into effect from March 2020 onwards. Obesity is considered a risk factor for a COVID-19 infection but also a risk factor for a more severe disease course resulting in higher mortality rates [ 17 , 18 ]. Both Dutch and international studies found that 70–90% of all COVID-19 patients admitted to Intensive Care Units with respiratory failure, were overweight [ 19 , 20 ]. The immune system of patients with obesity is less capable of fighting viruses and bacteria. Lifestyle improvements lead to improvements in the immune system [ 21 ], which might be an additional reason for deployment of the CLI for overweight people.

We expected the pandemic, and the measures aimed to curb it, to have an impact on the CLI-participants [ 22 ]. As the severity of the disease course increased for overweight patients, it led to more stress in this high-risk population [ 23 ]. The COVID-19 pandemic resulted for some people in a higher sense of urgency to start with a weight reduction program. Others, on the other hand, were hesitant to attend group meetings due to their high-risk profile related to a potential COVID-19 infection. The consequences of the COVID-19 restrictions such as a temporary curfew, closing (sports) facilities, working at home and wearing face masks in public areas led to feelings of loneliness but also impacted lifestyle routines [ 23 ]. In addition, CLIs were initially temporarily suspended, pending guidelines on restricted human contact. Some CLI-groups were permanently closed, others restarted in digital modus, providing additional challenges for both coaches and participants. COVID-19 shifted priority for caretakers and participants as there were shortages of staff due to sickness or deployment in more critical roles, impacting availability and attendance of (digital) CLI-sessions [ 24 ].

Therefore, the subobjective of this study was to investigate the effect of COVID-19 implications and restrictions on the intervention outcomes.

CooL-intervention

The CooL-intervention aims for higher perceived quality of life, healthier eating habits (including a focus on healthy food choices, food quantities and eating with attention), more physical activity, less sedentary behaviour, attention for high quality sleep and relaxation, and positive changes in physical outcomes such as weight, BMI and waist circumference. CooL includes an intake (1 h), a behavioural change phase of eight months (phase 1) with a follow-up phase of sixteen months (phase 2). The intervention consists of a combination of individual sessions (six hours in total, divided in 6 to 12 sessions depending on the preferences of the participant and coach) and 16 group sessions (1, 5 h each) all led by one and the same coach. Phase 1 and phase 2 both include eight group sessions with a higher density of sessions in phase 1 compared to phase 2 [ 15 ].

The CooL-intervention is an open CLI, which means that CooL has no strict protocol. Instead, it allows CooL-coaches to adapt the intervention to their target audience and context, within certain boundaries and restrictions. Participants pursue a predefined set of final objectives on knowledge and skills, supported by the coach who secures the main effective elements (e.g., goal setting, mobilizing social support, modelling, self-management and self-monitoring) of the CooL-intervention in implementation [ 15 ]. The CooL-coaches are trained and licensed professionals who coach participants to take responsibility for their personal lifestyle changes by addressing motivation, personal objectives and behavioural change. Participants are stimulated and supported towards more self-steering and self-management by identifying, mapping and putting personal health related behaviour into action. The main objective is to coach and activate participants to a sustained healthier lifestyle in line with their individual needs and goals.

CooL-intervention during COVID-19

The COVID-19 implications and restrictions resulted in adaptations in the way CooL was offered to participants. Some participants finalized the first eight months of CooL completely, before COVID-19 broke out in the Netherlands, others participated in CooL during the COVID-19 pandemic and measures. The first infection was detected in the Netherlands on February 27th, 2020, the first regional restrictions were imposed on March 6th and the ‘intelligent lockdown’ (a semi-lockdown with free human movement but restricted human contact) was introduced as of March 23th [ 25 ]. We used a cut-off date of April 1st, 2020, as participants finishing phase 1 of CooL before this date will have suffered limited to no impact on their lifestyle which cannot be guaranteed for participants finishing phase 1 of CooL after April 1st, 2020. By means of the cut-off date we distinguished between participants that were potentially impacted by COVID-19 while participating in CooL and participants that were not impacted by COVID-19.

The way in which CooL was offered, changed during the COVID-19 pandemic. These changes were inventoried by an additional survey among CooL-coaches and by adding questions related to COVID-19 to the existing CooL-questionnaire.

The open character of CooL provided ample opportunity for CooL-coaches to make adaptations to the content of the intervention, e.g., providing room for pressing topics like COVID-19 or COVID-19-related stress. In addition, the temporary expansion in the CLI-regulations in terms of health insurance coverage made it possible to offer CooL digitally instead of via face-to-face contact only [ 26 ].

Observations from daily practice showed that COVID-19 resulted in higher dropout rates, resulting in financial consequences for the coaches and motivational challenges for the remaining group members and the coach. Some CooL-coaches completely quit executing CooL due to uncertainty, loss of motivation and/or resistance to online coaching thereby leaving their participants no other option than to quit CooL. Others decided to start up CooL, as COVID-19 caused an income drop for self-employed coaches and the CLI offered a basic and stable income. This observed impact of COVID-19 on coaches and participants of CooL, gave rise to the initiation of this subobjective.

Study design and population

As CooL is part of regular health care, a control group receiving no treatment would be unethical, making a descriptive case series study the most appropriate study design in the Dutch context. The participants, all Dutch-speaking adults living in the Netherlands, were included from November 2018 until October 2021 at different locations throughout the Netherlands. Almost all participants met the inclusion criteria for participating in a CLI. In some cases (n = 5, 0.3%), BMI at baseline was below the inclusion threshold, potentially due to lifestyle changes in the time between participant’s application and the start of CooL. Since the waist circumference of these participants was above the threshold for inclusion, these cases were included.

Data collection

We used a questionnaire and anthropometric measurements to collect a broad set of data. The questionnaire was partly based on existing validated questionnaires [ 27 , 28 , 29 ], and partly based on input from a focus group session with the Dutch Association of Lifestyle coaches (BLCN) to define questions that match the scope and working method of the lifestyle coach with a strong focus on manageability of the questionnaire, as CooL is part of basic healthcare. The outcome measures we collected can be divided into the categories anthropometrics (i.e., weight/BMI and waist circumference), control and support (i.e., self-mastery and social support), physical activity (i.e., sedentary time on least/most active days and active minutes), diet attentiveness (attentiveness to meal composition, awareness to amounts of food and attentiveness to consuming), alcohol use and smoking, perceived fitness (i.e., perceived health, perceived fitness when waking, perceived fitness during daytime and impact of stress on daily functioning), sleep and stress.

During the course of the study, the questionnaire was adjusted with textual simplifications in both questions and answers preserving the original essence as much as possible and extended with additional questions covering changes in context (e.g., COVID-19). We collected information on the initiation of CooL during COVID-19, i.e., a digital start or a physical (face-to-face) start, and on the continuation mode of the sessions.

Data were collected at three time points during the CooL-intervention: at the beginning of the intervention, during the intake (T0); after 8 months, at completion of phase 1 of the intervention (T1); and after 24 months, at completion of the intervention (T2). Data from T2 were not yet available at the time of the analysis and are not presented in this article.

Demographics

At baseline, participants were asked to report their personal characteristics such as gender, date of birth, country of birth and highest completed education, marital status, living situation and occupational status. Educational level was categorized as low (i.e., no education, primary education or junior secondary education), intermediate (e.g., senior secondary education) and high (e.g., higher professional and vocational education or university) according to the definitions of the Dutch Central Bureau of Statistics [ 30 ]. The living situation was divided into living together with someone (married or cohabiting) with or without kids and living alone (divorced, unmarried, or widowed) with or without kids. The occupational status was categorized as: working (paid work, voluntary work or self-employed) and not working (homemaker, unemployed/job seeker, retired/in early retirement, disabled or student). Country of birth was categorized into Dutch or non-Dutch.

Anthropometrics

Normally anthropometric data (weight, length and waist circumference) are being measured by the CooL-coaches with professional equipment according to the guidelines provided by the Dutch Association of General Practitioners (Dutch: Nederlands Huisartsen Genootschap, NHG) [ 31 ]. Body weight (kg) was measured in kilogram, rounded off the nearest decimal. Height (m) was measured to the nearest centimetre without shoes. Waist circumference measurements were obtained to the nearest centimetre with a tape measure.

Control and support

Changes in self-management, of which self-mastery is an important aspect, are related to changes in quality of life and self-efficacy [ 32 ]. Self-mastery is defined by Pearlin as the extent to which one regards one’s life-chances as being under one’s own control in contrast to being fatalistically ruled [ 33 ]. The self-mastery questions in the questionnaire were based on the short version of the Pearlin Mastery Scale using four questions (for example “I have little control over the things that happen to me”) and a 5-point Likert scale ranging from strongly agree (1) to strongly disagree (5) [ 27 ]. To identify social support, we questioned the perceived support of close ones using a 5-point Likert scale ranging from no support at all (1) to a lot of support (5).

Physical activity

The outcome measurements on physical activity, diet and perceived fitness were defined in cooperation with the BLCN with the objective to capture the essence and map the desired outcomes of lifestyle coaching in a minimum set of questions. Physical activity was assessed with questions on sedentary behaviour, both on most and least active days (“What is the average number of hours you spent sitting on the day of the week you sit the most?”) and the number of physical activity minutes per day (“What is the average minutes per day that you are physically active (in minimum bouts of 10 minutes)?”).

Diet attentiveness, alcohol use and smoking

We defined questions on diet attentiveness, in line with the input of the BLCN, based on the knowledge that deliberate behaviour changes start with awareness. We used questions on the awareness of participants towards meal composition (How much attention do you usually pay to what you eat?) and meal quantities (How aware are you usually of the amount you eat?) and awareness during the actual consumption of food (With how much attention do you usually eat?) using a 5-point Likert scale from very little attention (1) to a lot of attention (5). In addition, we asked the number of units of alcohol consumed and units smoked per day.

Perceived fitness

Perceived fitness existed of questions, in line with the input of the BLCN, on perceived fitness when waking up and during the day, the impact of stress on daily functioning and on perceived health (i.e., feeling good about oneself, the extent of self-care invested and the perception of one’s general health). Questions were answered using a 5-point Likert scale, ranging from not good at all (1) to very good (5).

We defined a specific set of questions around the sub-constructs: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication and daytime dysfunction, analogous to the validated and widely used PSQI-questionnaire [ 28 ]. Each subconstruct was covered by one or two question(s) using a numerical value or a 4-point Likert scale, ranging from ‘never’ (1) to ‘three times per week or more frequently’ (4).

For stress, the validated Perceived Stress Scale questionnaire was used, which exists of ten questions using a 5-point Likert scale from never (1) to always (5) [ 29 ].

We used a brief survey for the lifestyle coach in retrospect to collect data on the way CooL was offered during COVID-19. The questions were related to the start date of the intervention derived from the date of the intake, the way the intervention was offered and the mode in which the intervention was started (e.g., starting in face-to-face mode versus digital mode). We used four categories to distinguish the way the intervention was offered: face-to-face sessions only, digital sessions only, a combination with more face-to face than digital sessions and a combination with more digital than face-to-face sessions.

As a first step, we recoded some of the variables to facilitate interpretation in the sense that a higher/positive score refers to a desirable trend and a lower/negative score to an undesirable trend in the variable. For constructs based on validated questionnaires (i.e., self-mastery, sleep and stress) we adopted the accompanying approach. Secondly, we performed an exploratory factor analysis using R software and calculated McDonald’s omega to assess the internal structure of items regarding the constructs perceived health (T0: 0.66, T1: 0.75), self-mastery (both T0 and T1: 0.71), sleep (T0: 0.76, T1: 0.74), stress (T0: 0.87, T1: 0.88) and motivation (T0: 0.25). These analyses justified summarizing the lifestyle constructs by item score means for all, except the construct motivation.

For all items and constructs, we ran descriptive analyses (e.g., means, standard deviations). Changes in outcome measures over time were analysed using paired t-tests (T1 versus T0). Effect sizes were calculated using Cohen’s d and the outcomes were interpreted in accordance with Lipsey’s guidelines for each pair of outcomes, i.e., an effect size smaller than or equal to 0.32 is considered small, an effect size between 0.33 and 0.55 is considered medium and an effect size of 0.56 or above is considered large [ 34 ]. To improve comprehensibility effect sizes are represented such that positive values represent change in the desired direction whereas negative values represent change in the undesired direction.

To be considered successful, the target for the CLI (including CooL) is a 5% weight loss after the two years intervention, as set by the Dutch Healthcare Institute (Dutch: Zorginstituut) based on the guidelines set up by the Dutch Institute for Quality in Health Care (Dutch: CBO) as well as their English counterpart (NICE) [ 35 ]. The data in this study covers the first phase of CooL only (8 months), still leaving 16 months to further extend weight loss. We categorized the outcomes on weight: 5% weight loss or more, between 0 and 5% weight loss, weight stabilization or weight gain to map the percentage of participants with weight loss.

Next, we split the dataset in two subgroups: we used the cut-off date of April 1st, 2020, to distinguish between the subgroups pre-COVID and during-COVID. The cut-off date was based on the date the intervention started and derived from that, the date the participants finished phase 1 of CooL. This distinction enabled comparison of differences from T0 to T1 between participants that were potentially impacted by COVID-19 and those that were not impacted by COVID-19. For all these differences we performed independent T-tests comparing subgroups. All T-tests were performed using SPSS- software (version 27). We used a threshold value of p = 0.05 for all t-tests. Missing data were excluded from the statistical analyses because these cases could not be included in the calculation of the differences between T0 and T1.

To explore the assumption that small behavioural changes sum up to medium and large effects in anthropometrics, a post-hoc sensitivity analysis was performed to compare the trend in changes (desired, neutral or undesired) in behaviour components to the trend in changes (desired, neutral or undesired) in the outcome components weight/BMI and waist circumference.

This study was submitted to and approved by the Research Ethics Committee of the Faculty of Health, Medicine and Life Sciences of Maastricht University (FHML-REC/2019/073). All participants gave their informed consent for their anonymised personal data to be used for research purposes.

Participants demographics

A total of 1824 adults participated between November 2018 and October 2021 (dataset A, see Fig.  1 ).

Of all participants a total of 28% were male and 72% female. This ratio is in line with the data from the national CLI-monitor [ 36 ]. Most participants (95%) were born in the Netherlands. Two third of the participants had a lower or intermediate level of education; 25% did not have a steady job (anymore) and over 70% of the participants were living together with a partner (Table  2 ).

Subgroups cool during COVID-19

We defined subgroups of participants that were potentially impacted by COVID-19 and participants that were not (see Table  3 ). A total of 120 participants (7%) finished phase 1 before April 1st, 2020. Most participants (n = 1667, 91%) finished the first phase of CooL after April 1st, 2020, which implies that those participants were potentially affected by the COVID-19 implications and measures when participating in CooL. Both subgroups of respondents are included in dataset B (see Fig.  1 ).

figure 1

Flowchart of study participants

From roughly a quarter of the participants (24%) with a runtime during COVID-19 we received information from the coaches on the way CooL was offered during COVID-19 by means of an additional survey (see Fig.  1 ). 80% of these participants started in face-to-face (physical) mode whereas 20% started digitally. Almost all participants received the individual and group sessions in CooL through a combination of physical and digital mode. Most participants received more physical than digital sessions (83% of the physical starters, 52% of the digital starters), followed by more digital than physical sessions (14% of the physical starters, 47% of the digital starters), 2% of all participants received physical sessions only and 0.5% digital sessions only.

Results on all items and constructs

In the result section all outcomes and effect sizes on the complete dataset (A) are mentioned. When comparing the outcomes of participants pre and during COVID-19 (dataset B) significant findings are mentioned in the text with the corresponding p-value. Table  4 displays all outcome measurements both on dataset A and dataset B including mean values and standard deviations as well as confidence intervals on changes in outcomes.

Weight, BMI and waist circumference all showed a decrease after eight months (T1) compared to baseline (T0). The BMI of the participants was on average 35.97 at T0 and decreased with 1.16 BMI-points at T1. The average weight loss was 3.44 kg at T1, corresponding to a 3.2% average weight loss per participant after eight months. In total 72% of the participants lost weight. 29% lost more than 5% at T1. The average waist circumference of the participants decreased from 116.3 cm at T0 to 112.4 cm at T1. The change in waist circumference demonstrated a medium effect size (0.50) at T1, whereas weight and BMI and showed a large effect size at T1 (0.57 and 0.58 respectively).

Participation in CooL during versus pre COVID-19 did not show a significant difference on weight, BMI or waist circumference of the participants.

Self-mastery showed a decrease at T1 compared to baseline with a small effect size (0.10) in the desired direction. Social support showed no change over time.

Differences in outcomes between participants pre versus during COVID-19 were present for self-mastery with a bigger change for participants pre COVID-19 (p = 0.01).

Sedentary time decreased at T1 both for least and most active days of the week: participants spent on average 49 min less sitting on least active and 34 min less sitting on most active days compared to baseline. The average daily active minutes (in minimum bouts of 10 min) increased from 95 min at T0 to on average 108 min at T1. The effect size on both sedentary (0.25 for least active days and 0.18 for most active days) and active time (0.15) was small.

Comparing the outcomes pre and during COVID-19: participants during COVID-19 showed a decrease in sedentary time compared to baseline whereas participants pre COVID-19 showed a small increase for both least active and most active days (p < 0.02). No difference between both subgroups could be detected on physical active minutes.

Diet attentiveness, alcohol and smoking

Over time the participants showed an increase in attentiveness for meal composition, awareness for the amounts of food selected and attentiveness when consuming food. In addition, the participants showed a decrease in alcohol consumption. The effect size on attentiveness for meal composition and consuming food was medium-sized (0.43 and 0.39 respectively), the effect size for the awareness of the amounts of food selected was large (0.58) and the effect size for the decrease in alcohol consumption was small (0.19) when comparing baseline to T1. Smoking showed no effect on T1 compared to baseline.

The outcomes of participants pre COVID-19 versus during COVID-19 showed no difference on the diet related outcomes, alcohol consumption or smoking.

The perceived fitness factors perceived health, feeling fit when waking up, feeling fit during the day and the impact of stress on daily functioning all showed an effect in the desired direction with a small effect size (between 0.05 and 0.28), except for perceived health which showed a medium effect size (0.50) at T1.

The subgroup comparison showed larger effects from baseline to T1 for participants pre COVID-19 compared to during COVID-19 on all perceived fitness factors (p < 0.02).

Sleep and stress

The constructs sleep and stress both showed a decrease at T1 compared to baseline with a small effect (0.30 and 0.23 respectively) in the desired direction.

The outcomes of participants pre COVID-19 showed a larger reduction at T1 in stress perception compared to the outcomes of participants during COVID-19 (p < 0.01). No differences were found between both subgroups on sleep.

Post-hoc sensitivity analysis

The post-hoc sensitivity analysis showed that on individual level, in general the trend in components related to behaviour (i.e., physical activity, diet attentiveness, sleep and stress) had a similar pattern as the trend in anthropometric outcomes except for smoking and sleep. In short, more physical active minutes, more attentiveness to diet and improved stress management are related to weight loss in CooL.

In this study we analysed changes in various outcomes on participants after eight months of the CooL-intervention as well as differences in outcomes between participants pre and during COVID-19. Looking at the changes in outcomes over eight months of CooL, the analyses showed positive changes compared to baseline. The largest effect sizes were found on weight, BMI, waist circumference, perceived health and diet attentiveness (i.e., attentiveness to meal composition, awareness to amounts of food and attentiveness to consuming). Changes in behaviour and perceived fitness varied between small and medium effect size, whereas changes in anthropometrics showed a medium to large effect size.

Encouraging participants to take responsibility for their personal lifestyle is an essential element of CooL. Participants prioritize their health-related behaviours and define personal actions. The consequence of this set-up is that all participants start working on a behavioural aspect of their choice, which may lead to changes that are averaged out when looking at a population level. The timeframe of this study only covers the first eight months of the intervention implying that participants might not yet have initiated changes in all health-related behavioural domains. Note that during the first eight months of the study, major changes were already found on anthropometrics and perceived health. It is plausible that these small behavioural changes together sum up to medium and large-sized changes in anthropometric outcomes and perceived health. The post-hoc sensitivity analysis gives support to the assumption that the behavioural changes correlate with changes in anthropometrics. Two exceptions are smoking and sleep: in many cases people that quit smoking, gain weight during the first few months of abstinence [ 37 ] and the relation between sleep-related behaviour and weight is likely to be more indirect (i.e., via hormonal pathways and other behaviours) [ 12 ].

The average weight loss per participant was 3.2%, with 29% of the participants losing 5% or more. This corresponds with a decrease of 1.16 points in BMI and an average decrease of 3.44 kg after the first eight months of CooL. Compared to previous research on the CooL-pilot [ 15 ], HealthyLIFE-study [ 16 ] (with respectively an average decrease in weight of 2.3 and 2.4 kg) and research on similar interventions [ 13 , 14 , 38 ], these results are promising. Future research on the two-year results is needed to determine the effect of the CooL-intervention on the total duration of 24 months.

The outcomes of participants pre COVID-19 versus during COVID-19 showed differences only on self-mastery (p = 0.01), sedentary time (all underlying constructs p < 0.02), perceived fitness (all underlying constructs p < 0.02) and stress (p < 0.01). The differences found are partly in line with previous research: a larger decrease of perceived stress when participating pre COVID-19, is in line with the findings of Ammar [ 39 ]. Ammar identified a negative effect on mental-wellbeing, on mood and feelings during COVID-19 [ 39 ]. Especially vulnerable populations have been found to show an increase in stress [ 40 , 41 ]. For alcohol usage and smoking two opposite outcomes were seen during COVID-19: an increase due to distress or boredom and a decrease in usage linked to prevention and health withstanding the threat of COVID-19 or limited access and resources [ 42 , 43 ]. On population level, increases in alcohol usage for some people even out with decreases in alcohol usage for others, leading on population level to changes in alcohol usage close to zero [ 42 ]. A similar reasoning for smoking could explain that no effect on alcohol and smoking was seen for the CooL-participants during COVID-19 [ 43 ]. However, the comparison of this intervention study in active participants with population-level observational studies should be done with great caution as participating in an intervention can trigger behaviour change on lifestyle related topics including alcohol and smoking.

There are also several findings that are not in line with previous research: firstly, research on the effect of COVID-19 on sleep in several European countries showed delayed sleep timing, more time spent in bed and impaired sleep quality [ 44 , 45 ]. It also showed large individual differences in perceived sleep quality mainly depending on pre-pandemic sleep quality. In general, negative affect and feelings of worry linked to COVID-19 restrictions, were associated with changes in sleep quality [ 44 , 45 ]. In contrast, the present study showed that the improvements in perceived sleep quality did not differ prior versus during COVID-19.

Secondly, other studies on the impact of COVID-19 on lifestyle-related behaviour have shown that most health behaviours were largely affected by the pandemic and its related measures. Regarding diet, Huber et al. [ 46 ] showed an increase in food consumption, especially for overweight people. Furthermore, the majority of studies have shown a decrease in physical activity and an increase in sedentary behaviour during COVID-19 lockdowns across several populations [ 25 , 47 , 48 ]. The CooL subgroup analysis showed no differences for both diet and physical activity between the runtime of CooL pre versus during COVID-19. The changes in sedentary time were even more desirable for participants in CooL during the pandemic. In times of a major pandemic consistency in behaviour and/or small improvements in behaviour are likely to be a huge win.

The effect of CooL on the three anthropometric outcomes was not affected by COVID-19 as the subgroup analyses showed no difference between participation in CooL pre or during COVID-19. This is a striking result given the outcomes of previous research on this topic: two studies on weight change during COVID-19 pandemic indicated an average weight gain of 1.5 to 2 kg [ 49 , 50 ], whereas an online questionnaire in The Netherlands even showed an average weight gain of 5.6 kilos [ 51 ]. Overall, the results of this study indicate that the effect on the anthropometric outcomes of the CooL-participants were not affected by COVID-19. Participating in the CooL-intervention may thus have protected against relapsing to unhealthier behaviour despite a decreased sense of self-mastery and increased stress.

Limitations and strengths

During the time of the study the questionnaire was subject to minor revisions. We intended to keep the scope of the questions and answers similar for all versions, but we cannot rule out an effect on the study outcomes. However, as with any observational study, differences in outcomes could also be due to differences in demographics, zeitgeist and the emergence of COVID-19.

The sudden emergence of COVID-19 was unforeseen and can be considered a limitation of the study as it impacted the intervention and outcomes in many ways. At the same time, it can be regarded as an opportunity to study the effects of a large-scale health promotion intervention during a pandemic.

The lack of a control group inhibits us to draw strong conclusions on the effectiveness of the intervention. Results indicate changes in outcomes over time, but inferences regarding intervention effectiveness need to be interpreted with caution.

Motivation was questioned using a scale derived from Self-Determination Theory with six questions. The exploratory factor analysis did not justify summarizing the motivational items in one construct by item score means. Consequently, we looked at these motivation items separately instead of using one summarizing construct, in line with Chemolli and Gagné [ 52 ]. However, this approach led to uninterpretable results. Anecdotal evidence collected by feedback from participants and coaches indicated that the motivational questions caused confusion and were considered difficult to interpret for participants. This led to a major revision of the measurement of this construct in a new version of the questionnaire for future data collection and research. Physical activity, diet attentiveness, smoking and alcohol use were asked in retrospective via questionnaires which entails the risk of overestimation. However, whenever possible we used multiple questions that allowed for cross-checking. In addition, we looked at the difference between T0 and T1, which probably led to an overestimation in both measurements, i.e., with less risk of overestimation in the change scores. Furthermore, we used the same measurements for these constructs in previous studies, supporting comparability.

Despite all attempts to collect additional data, we did not receive enough data to draw strong conclusions on the different ways of implementing CooL during COVID-19 (e.g., digital versus physical contact and starting in digital versus face-to-face mode) and only on whether it was implemented before or during COVID-19. In retrospect, we found that the ratio of participants who started before COVID-19 to those who started during COVID-19 was off balance, but this mainly reflects the number of participants who completed the first phase of CooL in a given period. To draw strong conclusions on the different ways of implementing CooL in digital mode, more data is needed on various implementation modes of CooL. A total of 37 participants in the overall dataset could not be assigned to the subgroups pre or during COVID-19 leading to slightly deviating average outcomes in the subsamples.

In normal conditions anthropometrics are measured by the CooL-coach in order to minimize self-report bias. As COVID-19 restrictions could have changed the measurement method, additional information was gathered from the CooL-coaches that were the main data suppliers (representing data of a quarter of the participants, n = 490). This information indicated that in general, physical measurements took place either by the coach or on a distance of 1.5 m under direct supervision of the coach.

Future recommendations

This study provides insights on the outcomes after participating eight months in CooL and on the possible influence of COVID-19 on the outcomes, but it also provides input on recommendations for future research on CooL and adaptations to the questionnaires used for CooL:

Validation research of the question on social support and the questions on diet attentiveness as well as the newly constructed questions on motivation, initiated by the desire to validate the measurement instruments on these constructs.

Development of an equally effective online CooL-intervention, preserving the existing working elements and objectives of CooL as much as possible.

Effect study of CooL after 24 months participation (including the outcomes on phase 1 and phase 2).

Addition of the CooL questionnaire with questions on the mode of delivery of CooL (physically or digitally).

Conclusions

After eight months of CooL, large effect sizes on changes in anthropometrics and perceived health were found, irrespective of participation during the COVID-19 pandemic. The results show that small changes in multiple behaviours go along with a large positive change in perceived health and health-related outcomes in line with the lifestyle coaching principles. Participating in the CooL-intervention may have protected against engaging in unhealthier behaviours during the pandemic, despite a decreased sense of self-mastery and increased stress.

Data availability

The datasets generated and/or analysed during the current study are not publicly available because the informed consent statement to using data at the individual level was limited to the authors of this article and are only available from the corresponding author on reasonable request.

Abbreviations

Beroepsvereniging Leefstijlcoaches Nederland

Body mass index

Centraal Begeleidingsorgaan

Combined lifestyle intervention

Coaching op Leefstijl

Corona virus disease 19

Ethics review committee of the faculty of health, medicine and life sciences

Nederlands Huisarts Genootschap

National institute for health and care excellence

Pittsburgh sleep quality index

Rijksinstituut voor Volksgezondheid en Milieu

Statistical package for the social sciences

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Acknowledgements

The authors like to thank the CooL-coaches and the CooL-participants for their efforts to make this research possible.

No funding was provided for this research.

Author information

Ester Janssen and Nicole Philippens are joint first authors.

Authors and Affiliations

Department of Health Promotion, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands

Ester Janssen, Nicole Philippens & Stef Kremers

Department of Health Promotion, CAPHRI, Care & Public Health Research Institute, Maastricht University, Maastricht, The Netherlands

Rik Crutzen

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Contributions

Conceptualization, N.P., E.J., R.C. and S.K.; Methodology, N.P., E.J., R.C. and S.K.; Validation, R.C. and S.K.; Formal Analysis, N.P., E.J. and R.C; Investigation, N.P. and E.J.; Data Curation, N.P. and E.J.; Writing—Original Draft Preparation, N.P. and E.J.; Writing—Review and Editing R.C., and S.K.; Visualization, N.P. and E.J.; Funding Acquisition, not applicable. Both E.J. and N.P. contributed equally to the study.All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Ester Janssen .

Ethics declarations

Ethics approval and consent to participate.

This study was conducted according to the guidelines of the Declaration of Helsinki. This study was submitted to and approved by the Research Ethics Committee of the Faculty of Health, Medicine and Life Sciences of Maastricht University (FHML-REC/2019/073). Informed consent was obtained from all subjects involved in this study. All participants in CooL are adults except one minor who started the intervention at the age of 16. In the Netherlands adolescents from the age of 16, have the legal right to decide for themselves on medical treatments as they have the same patient rights as adults.

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Not applicable.

Competing interests

Both main authors (E.J. and N.P.) are co-owner of the CooL-intervention. Not applicable for S.K. and R.C.

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Janssen, E., Philippens, N., Kremers, S. et al. Outcomes of the combined lifestyle intervention CooL during COVID-19: a descriptive case series study. BMC Public Health 24 , 40 (2024). https://doi.org/10.1186/s12889-023-17501-x

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DOI : https://doi.org/10.1186/s12889-023-17501-x

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Changes in Hospital Adverse Events and Patient Outcomes Associated With Private Equity Acquisition

  • 1 Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston
  • 2 Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
  • 3 Department of Health Care Policy, Harvard Medical School, Harvard University, Boston, Massachusetts
  • 4 Department of Public Health Sciences, University of Chicago, Chicago, Illinois
  • 5 Department of Medicine, Massachusetts General Hospital, Boston
  • 6 Center for Primary Care, Harvard Medical School, Harvard University, Boston, Massachusetts
  • Viewpoint Potential Implications of Private Equity Investments in Health Care Delivery Suhas Gondi, BA; Zirui Song, MD, PhD JAMA
  • Viewpoint A Policy Framework for the Growing Influence of Private Equity in Health Care Delivery Christopher Cai, MD; Zirui Song, MD, PhD JAMA
  • Medical News in Brief Private Equity Ownership in Health Care Linked to Higher Costs, Worse Quality Emily Harris JAMA
  • Original Investigation Changes in Hospital Income, Use, and Quality Associated With Private Equity Acquisition Joseph D. Bruch, BA; Suhas Gondi, BA; Zirui Song, MD, PhD JAMA Internal Medicine
  • Insights COVID-19 and Private Equity Investment in Health Care Delivery Joseph Bruch, BA; Suhas Gondi, BA; Zirui Song, MD, PhD JAMA Health Forum

Question   How do quality of care and patient outcomes change after private equity acquisition of hospitals?

Findings   In a difference-in-differences examination of 662 095 hospitalizations at 51 private equity–acquired hospitals and 4 160 720 hospitalizations at 259 matched control hospitals using 100% Medicare Part A claims data, private equity acquisition was associated with a 25.4% increase in hospital-acquired conditions, which was driven by falls and central line–associated bloodstream infections. Medicare beneficiaries at private equity hospitals were modestly younger, less likely to have dual eligibility for Medicare and Medicaid, and transferred more to other acute care hospitals relative to control, likely reflecting a lower-risk population of admitted beneficiaries. This potentially explained a small relative reduction for in-hospital mortality that dissipated by 30 days after hospital discharge.

Meaning   Private equity acquisition of hospitals, on average, was associated with increased hospital-acquired adverse events despite a likely lower-risk pool of admitted Medicare beneficiaries, suggesting poorer quality of inpatient care.

Importance   The effects of private equity acquisitions of US hospitals on the clinical quality of inpatient care and patient outcomes remain largely unknown.

Objective   To examine changes in hospital-acquired adverse events and hospitalization outcomes associated with private equity acquisitions of US hospitals.

Design, Setting, and Participants   Data from 100% Medicare Part A claims for 662 095 hospitalizations at 51 private equity–acquired hospitals were compared with data for 4 160 720 hospitalizations at 259 matched control hospitals (not acquired by private equity) for hospital stays between 2009 and 2019. An event study, difference-in-differences design was used to assess hospitalizations from 3 years before to 3 years after private equity acquisition using a linear model that was adjusted for patient and hospital attributes.

Main Outcomes and Measures   Hospital-acquired adverse events (synonymous with hospital-acquired conditions; the individual conditions were defined by the US Centers for Medicare & Medicaid Services as falls, infections, and other adverse events), patient mix, and hospitalization outcomes (including mortality, discharge disposition, length of stay, and readmissions).

Results   Hospital-acquired adverse events (or conditions) were observed within 10 091 hospitalizations. After private equity acquisition, Medicare beneficiaries admitted to private equity hospitals experienced a 25.4% increase in hospital-acquired conditions compared with those treated at control hospitals (4.6 [95% CI, 2.0-7.2] additional hospital-acquired conditions per 10 000 hospitalizations, P  = .004). This increase in hospital-acquired conditions was driven by a 27.3% increase in falls ( P  = .02) and a 37.7% increase in central line–associated bloodstream infections ( P  = .04) at private equity hospitals, despite placing 16.2% fewer central lines. Surgical site infections doubled from 10.8 to 21.6 per 10 000 hospitalizations at private equity hospitals despite an 8.1% reduction in surgical volume; meanwhile, such infections decreased at control hospitals, though statistical precision of the between-group comparison was limited by the smaller sample size of surgical hospitalizations. Compared with Medicare beneficiaries treated at control hospitals, those treated at private equity hospitals were modestly younger, less likely to be dually eligible for Medicare and Medicaid, and more often transferred to other acute care hospitals after shorter lengths of stay. In-hospital mortality (n = 162 652 in the population or 3.4% on average) decreased slightly at private equity hospitals compared with the control hospitals; there was no differential change in mortality by 30 days after hospital discharge.

Conclusions and Relevance   Private equity acquisition was associated with increased hospital-acquired adverse events, including falls and central line–associated bloodstream infections, along with a larger but less statistically precise increase in surgical site infections. Shifts in patient mix toward younger and fewer dually eligible beneficiaries admitted and increased transfers to other hospitals may explain the small decrease in in-hospital mortality at private equity hospitals relative to the control hospitals, which was no longer evident 30 days after discharge. These findings heighten concerns about the implications of private equity on health care delivery.

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Kannan S , Bruch JD , Song Z. Changes in Hospital Adverse Events and Patient Outcomes Associated With Private Equity Acquisition. JAMA. 2023;330(24):2365–2375. doi:10.1001/jama.2023.23147

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Coral Davenport reported in the Pajaro Valley, meeting farmers, regulators and C.E.O.s and visiting oceanfront strawberry fields. Photographs by Nathan Weyland.

The strawberry, blackberry and raspberry fields of the Pajaro Valley stretch for 10 miles along the coast of California’s Monterey Bay, jeweled with fruit from April through early December. The valley’s 30,000 acres of farmland are also ruffled with emerald lettuces, brussels sprouts and varieties of kale, bringing in roughly $1 billion in revenue to the region each year.

All that abundance doesn’t come cheap.

While American farmers elsewhere have watered their crops by freely pumping the groundwater beneath their land, growers in Pajaro must pay hefty fees for irrigation water — making it one of the most expensive places to grow food in the country, if not the world. The cost: Up to $400 per acre-foot , a standard measurement equal to water covering one acre, one foot deep. The fees bring in $12 million a year, which is used to recycle, restore and conserve the region’s groundwater.

The Pajaro Valley’s unusual system — essentially a tax on water — was born of a berry-growing disaster some 40 years ago that forced farmers to act. Today, as the nation faces a spreading crisis of dwindling groundwater , stemming from a combination of climate change, agricultural overpumping and other issues, some experts say the Pajaro Valley is a case study in how to save the vital resource.

“What they are doing is cutting edge,” said Felicia Marcus, a former chair of the California State Water Resources Control Board and now a fellow at Stanford University’s Water in the West Program. While a few other regions have imposed fees on groundwater for farming, Pajaro Valley has been one of the most aggressive and effective. “They are way ahead of the curve,” she said.

Experts from as far away as China and Egypt are traveling to the valley to study the system. But replicating it elsewhere could face major challenges. For one thing, “People don’t like taxes,” said Nicholas Brozovic, an agricultural economist at the University of Nebraska. “There’s nothing mysterious about that.”

New research on the program revealed a direct connection between paying for the groundwater and conserving it: A 20 percent increase in the price of groundwater has resulted in a 20 percent decrease in the extraction of groundwater.

A man wearing a dark pullover sweater stands in a greenhouse between rows of plants.

Water can’t be free anywhere,” said Soren Bjorn of Driscoll’s, the berry giant.

A cream-colored office wall decorated with a row of artworks depicting blackberries, strawberries and other varieties of berry.

Driscoll’s headquarters in the heart of California berry country.

One reason experts see Pajaro as a model: Despite the high price of water, agriculture in the region is thriving. It is the headquarters of major brands, including Driscoll’s, the world’s largest berry supplier, and Martinelli’s, which grows most of the apples for its sparkling cider in the Pajaro Valley.

Soren Bjorn, a senior executive at Driscoll’s who in January will become the chief executive, said in an interview that he “absolutely” sees the region as a model of water pricing that could be replicated in water-stressed regions from Texas to Portugal. “Water can’t be free anywhere, because you can’t run a sustainable water supply without pricing it,” he said. “That would apply to the globe."

Yet, if the Pajaro Valley experiment were to be replicated across the country, it could trigger changes across the economy that affect both farmers and shoppers, resulting in higher prices at the grocery store while forcing farmers to abandon low-cost commodity crops that are needed for animal feed and other purposes, such as textiles.

While corporate growers of premium products like berries, which are shipped to the shelves of major chains like Whole Foods, Safeway and Trader Joe’s, can absorb the price of Pajaro’s water, there is no way farmers of commodity crops like cotton, alfalfa and soybeans can make the economics work, said David Sanford, the agricultural commissioner of the Santa Cruz County, which includes the Pajaro Valley.

In the years since the price on water was imposed, growers of those crops either shifted to high-priced berries and lettuces, or simply left the region for cheaper pastures.

“There’s a big public-policy argument for pricing groundwater,” said Louis Preonas, an agricultural economist at the University of Maryland. “But if you were to try something like this across the country, it would mean farmers would shift away from growing crops like corn, or leave agriculture altogether. Any way you cut it, it would likely raise food prices. But the alternative is running out of water.”

Tiny plants peek from rows of plastic sheeting that stretch to the horizon.

Decades ago, overpumping began imperiling Pajaro Valley farms. A strawberry field recently.

A New York Times investigation this year found that many of the aquifers that supply 90 percent of the nation’s drinking-water systems are being severely depleted by a combination of climate change and overpumping by farmers, industrial users, cities and others.

For many of the nation’s farming regions, the day of reckoning with the loss of groundwater is fast approaching. In the Pajaro Valley, it came 40 years ago.

With its loamy, sandy soil and cool nighttime breezes, the Monterey coast is an ideal climate for strawberries. But in the 1980s, disaster struck. Growers over pumped the coastal groundwater, allowing saltwater from the Pacific Ocean to seep in below their fields, up through the roots of the berry crop.

“You could see the yellow leaves, the discoloration, the stunted growth,” recalled Dick Peixoto, whose family has farmed here since 1920.

Faced with an economic disaster, Mr. Peixoto and other growers formed a local water agency with two goals: preserve the groundwater and prevent the state from taking control.

The Pajaro Valley Water Management Agency, still locally run today, got to work. Its first project was installing meters to measure how much groundwater growers were using. In 1993, it started charging farmers a modest fee of $30 per acre-foot to cover the cost of managing and reading the meters.

A man wearing a dark windbreaker and sunglasses stands in a field surrounded by bright green crops at waist height.

Dick Peixoto: “You could see the yellow leaves, the discoloration.”

A close-up image of the sealed end of an angled pipe protruding out of bare ground,

A well pipe capped years ago to stop saltwater intrusion.

The water agency hired hydrologists and other consultants, who concluded that the aquifer was severely overdrawn and could be lost entirely to saltwater. In response the agency built a $6 million project to capture and divert excess rainwater from a creek near the ocean and pump it into a storage basin, where it percolates into underground wells and is eventually used for irrigation.

Next came a $20 million water recycling plant, which cleans approximately five million gallons of sewage each day and sends it through a network of purple pipes to farm fields. The purple signals that the water inside is recycled.

Now the agency is building an $80 million system to capture and store more rainwater to be used for irrigation. Some of the cost the agency’s projects has been covered by federal grants and loans, with the rest from the groundwater pricing system, said Brian Lockwood, who has been the general manager of the Pajaro Valley Water Management Agency for 18 years.

“These projects are millions of dollars, and without this source of revenue they could never come to be,” he said.

As the ambitions of the water agency increased, so did the price of the water. It is scheduled to reach $500 per acre-foot by 2025.

In the early years, farmers chafed under the rate increases. “The pricing was really difficult, when the water used to be, you know, free,” said Thomas Broz, who has farmed about 75 acres in Pajaro since 1996.

Eventually, a group of growers challenged the water agency in court and were able to drive down the prices for a few years, and even forced the agency to refund about $12 million to farmers between 2008 and 2011.

Bright yellow construction equipment stands beside a excavated area that is partly lined with concrete.

A new reservoir to capture rainwater.

But then, from 2012 to 2017, California was struck by its worst drought in recorded history, parching farmland and devastating the rural economy. Growers across the state, particularly in the Central Valley, reached a deal with the state to sharply restrict their water use and fallow their fields.

In the Pajaro Valley, water became more expensive, but at least it was still flowing. To save money many Pajaro farmers invested in precision irrigation technology to distribute carefully measured water exactly where it was needed. Gone were the days of sprinklers that drenched fields indiscriminately.

In the midst of the drought, the then-governor of California, Jerry Brown, signed a law requiring every part of the state to devise a plan to conserve groundwater. Miles Reiter, the outgoing chief executive of Driscoll’s, spoke in support of the law.

Suddenly, Pajaro was a model.

“Now, we’re seen as these pioneers who showed the way,” said Mr. Lockwood. “We get calls from all over the state. How did you get this going? How do get the growers to agree to it?” He partly credits local control of the resources, saying, “This is better than the county or the state coming and taking control. And by now, this is something that’s solid, it’s been tried, it’s survived lawsuits.”

The last time the agency raised rates, in 2021, there was almost no resistance from growers, said Amy Newell, who chairs the Pajaro Valley Water Management Agency Board of Directors.

A man wearing a button-down shirt and dark vest gestures with his right hand above a purple water pipe next to a field.

Brian Lockwood: “We’re seen as these pioneers who showed the way.”

Overhead view of a white building laced with pipes across the rooftop.

The Pajaro Valley water treatment center.

Mr. Broz, who paid $20,000 last year for water, said he has come around to accepting the system.

“The farmer has very little flexibility to build in the cost of water, so it means we have to price it into our product — it means we basically can’t be as competitive,” said Mr. Broz, who grows lettuces, berries, apples, and other vegetables. “But the pricing has allowed us to put in place the kind of measures that will help us have a sustainable system for the long term, if we want to keep the resource.”

In the central California valley’s Westlands water district, where many farmers fought the groundwater-management law, the board of directors will soon vote on a plan that would allow growers to pay for credits to use groundwater above a certain allocation. They could buy and sell the credits, starting at about $200 a credit. A handful of other water districts in California are implementing similar measures.

Many farmers worry about the beginning of such a trend.

“The concern is that any kind of pricing scheme or market based mechanism that tries to manage or distribute this resource is likely to privilege a certain kind of producer — a multinational corporation — at the expense of small-scale independent farmers,” said Jordan Treakle, program coordinator for the National Family Farm Coalition.

And in some parts of the country, pricing groundwater could spell an end to current crops altogether. For example, some experts said that could be the case for producers of Texas cotton , a commodity crop that relies almost entirely on groundwater from the depleting Ogallala aquifer.

Mr. Bjorn of Driscoll’s said Americans should be ready to face just that outcome.

“We can’t get away with producing something for which the resources do not exist,” he said. “We would be fooling ourselves to keep growing low-value crops in places in the desert.”

“Overcoming the hump of the politics is the hardest part,” Mr. Bjorn said. “After that it’s just managing the resource.”

Uncharted Waters

A series on the causes and consequences of disappearing water.

America Is Using Up Its Groundwater Like There’s No Tomorrow

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Big Farms and Flawless Fries Are Gulping Water in the Land of 10,000 Lakes

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A Colorado City Has Been Battling for Decades to Use Its Own Water

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‘Monster Fracks’ Are Getting Far Bigger. And Far Thirstier.

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Inside Poland Spring’s Hidden Attack on Water Rules It Didn’t Like

A bottle of Poland Spring water, upside down with water pouring out, pictured against a black background.

A Tangle of Rules to Protect America’s Water Is Falling Short

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As Groundwater Dwindles, Powerful Players Block Change

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Airlines Race Toward a Future of Powering Their Jets With Corn

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Who Gets the Water in California? Whoever Gets There First.

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How America’s Diet Is Feeding the Groundwater Crisis

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More nonpregnant women are requesting abortion pills to have on hand

Boxes of Mifepristone.

Requests for abortion pills by women who were not yet pregnant spiked after a draft of the Supreme Court’s Dobbs decision leaked in 2022, a study published Tuesday in the journal JAMA Internal Medicine found. 

The study looked at data from Aid Access, a Netherlands-based nonprofit that provides access to abortion medications via telehealth. Aid Access was also involved with the study. 

At the time the data was collected, the organization connected patients with doctors from outside the U.S. who could prescribe the two drugs used in a medication abortion — mifepristone and misoprostol — which were then shipped to the patient at a cost of $110.

Usually, this is done for people who are already pregnant, but the researchers found that the number of women who sought the drugs pre-emptively, through a little-known process called advance provision, increased when federal protections for abortion and for access to medication abortion were threatened. 

“The surges or spikes in requests really coincide with threats to abortion access,” said the study’s lead author, Dr. Abigail Aiken, an associate professor of public affairs at the University of Texas at Austin. She said she expects requests to rise again this year, with mifepristone access before the Supreme Court . 

Between September 2021 and April 2023, Aid Access received more than 48,000 advance provision requests, according to the study. The women requesting the pills were more likely to be white, age 30 or older, and live in an urban area. 

After the Supreme Court’s Dobbs decision leaked — which indicated that the court planned to overturn Roe v. Wade and therefore end the constitutional right to abortion — requests spiked from about 25 per day, on average, to nearly 250, the study found. After the official Dobbs decision, Aid Access received about 90 requests per day. Requests spiked again — to an average of 172 per day — in April 2023, after opposing court rulings made it unclear whether mifepristone would still be available. 

“Advance provision is something that people in the United States seem to need or want, and it’s particularly pronounced in states that are considering, or may in the future, enact restrictions or bans regarding abortion,” Aiken said of the findings. 

Aiken said Aid Access is still getting advance provision requests, which are now filled by U.S.-based providers who live in states with shield laws that protect prescribers from facing legal ramifications for prescribing abortion medication to patients who live in states where abortion is restricted. 

Prescribing the drugs for medication abortion before a woman is pregnant can be controversial, and it’s not something every prescriber does.  

“The risks in prescribing before a person gets pregnant are only theoretical at this point,” said Dr. Emily Godfrey, an OB-GYN and family medicine doctor at UW Medicine at the University of Washington, who was not involved with the new study. 

Godfrey said that the medications are very safe, but that it’s unclear if a sometimes monthslong delay between receiving abortion medication through advance provision and taking the medication for an abortion could affect how it’s used. It’s possible that some women would not receive the support they need or may take the medication incorrectly.  

She added that the aim of providers is to cause the least harm to patients, and in some cases, this argument may be applied to prescribing abortion medication to someone who is not pregnant, but who may face severe complications if they become pregnant.

“We’re now talking about 16 million people at this point are traveling out of state for abortion care . If someone who is high-risk ends up pregnant, it would not seem out of the norm to prescribe mifepristone in advance, because you could argue you are doing something that would reduce patient harm,” Godfrey said. In other words, “prescribing it to people who don’t have access to safe abortion in a timely way.”

But Godfrey said the results of the new study gave her pause, because the people who may need advance provision abortion access most do not appear to be the ones accessing it.

“Those people who are ordering advanced provisions are not matching those most harmed by abortion restrictions: those who are younger, have fewer resources, who are systematically marginalized, and who tend to not be white,” she said. 

People who live in urban areas, who are white and who have higher incomes — those who were most likely to request the pills in the study — are typically those who have access to safe and timely abortion, which challenges advance provision as a harm-reduction strategy, Godfrey said. 

“It’s not necessarily meeting the people who are the most harmed or burned by lack of access to abortion care,” she said. 

For now, Godfrey does not expect advance provision to become commonplace among doctors in the U.S. 

“We are so focused on getting patients who desire or need an abortion an abortion when they really need it. I think finding a provider who will get you pills when you don’t yet need it will always be secondary at this point,” she said. 

Kaitlin Sullivan is a contributor for NBCNews.com who has worked with NBC News Investigations. She reports on health, science and the environment and is a graduate of the Craig Newmark Graduate School of Journalism at City University of New York.

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