qualitative and quantitative case study

Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Research Writing and Analysis

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

Writing a Case Study

What is a case study?

A Case study is: 

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

What are the different types of case studies?

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

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

Who are your case study participants?

What is triangulation ? 

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

How to write a Case Study?

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

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• Knowledge Base
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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

Prevent plagiarism, run a free check.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Raimo Streefkerk

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28 Case Selection for Case‐Study Analysis: Qualitative and Quantitative Techniques

John Gerring is Professor of Political Science, Boston University.

• Published: 02 September 2009
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This article presents some guidance by cataloging nine different techniques for case selection: typical, diverse, extreme, deviant, influential, crucial, pathway, most similar, and most different. It also indicates that if the researcher is starting from a quantitative database, then methods for finding influential outliers can be used. In particular, the article clarifies the general principles that might guide the process of case selection in case-study research. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. The article then draws attention to two ambiguities in case-selection strategies in case-study research. The first concerns the admixture of several case-selection strategies. The second concerns the changing status of a case as a study proceeds. Some case studies follow only one strategy of case selection.

Case ‐study analysis focuses on one or several cases that are expected to provide insight into a larger population. This presents the researcher with a formidable problem of case selection: Which cases should she or he choose?

In large‐sample research, the task of case selection is usually handled by some version of randomization. However, in case‐study research the sample is small (by definition) and this makes random sampling problematic, for any given sample may be wildly unrepresentative. Moreover, there is no guarantee that a few cases, chosen randomly, will provide leverage into the research question of interest.

In order to isolate a sample of cases that both reproduces the relevant causal features of a larger universe (representativeness) and provides variation along the dimensions of theoretical interest (causal leverage), case selection for very small samples must employ purposive (nonrandom) selection procedures. Nine such methods are discussed in this chapter, each of which may be identified with a distinct case‐study “type:” typical, diverse, extreme, deviant, influential, crucial, pathway, most‐similar , and most‐different . Table 28.1 summarizes each type, including its general definition, a technique for locating it within a population of potential cases, its uses, and its probable representativeness.

While each of these techniques is normally practiced on one or several cases (the diverse, most‐similar, and most‐different methods require at least two), all may employ additional cases—with the proviso that, at some point, they will no longer offer an opportunity for in‐depth analysis and will thus no longer be “case studies” in the usual sense ( Gerring 2007 , ch. 2 ). It will also be seen that small‐ N case‐selection procedures rest, at least implicitly, upon an analysis of a larger population of potential cases (as does randomization). The case(s) identified for intensive study is chosen from a population and the reasons for this choice hinge upon the way in which it is situated within that population. This is the origin of the terminology—typical, diverse, extreme, et al. It follows that case‐selection procedures in case‐study research may build upon prior cross‐case analysis and that they depend, at the very least, upon certain assumptions about the broader population.

In certain circumstances, the case‐selection procedure may be structured by a quantitative analysis of the larger population. Here, several caveats must be satisfied. First, the inference must pertain to more than a few dozen cases; otherwise, statistical analysis is problematic. Second, relevant data must be available for that population, or a significant sample of that population, on key variables, and the researcher must feel reasonably confident in the accuracy and conceptual validity of these variables. Third, all the standard assumptions of statistical research (e.g. identification, specification, robustness) must be carefully considered, and wherever possible, tested. I shall not dilate further on these familiar issues except to warn the researcher against the unreflective use of statistical techniques. 1 When these requirements are not met, the researcher must employ a qualitative approach to case selection.

The point of this chapter is to elucidate general principles that might guide the process of case selection in case‐study research, building upon earlier work by Harry Eckstein, Arend Lijphart, and others. Sometimes, these principles can be applied in a quantitative framework and sometimes they are limited to a qualitative framework. In either case, the logic of case selection remains quite similar, whether practiced in small‐ N or large‐ N contexts.

Before we begin, a bit of notation is necessary. In this chapter “ N ” refers to cases, not observations. Here, I am concerned primarily with causal inference, rather than inferences that are descriptive or predictive in nature. Thus, all hypotheses involve at least one independent variable ( X ) and one dependent variable ( Y ). For convenience, I shall label the causal factor of special theoretical interest X   1 , and the control variable, or vector of controls (if there are any), X   2 . If the writer is concerned to explain a puzzling outcome, but has no preconceptions about its causes, then the research will be described as Y‐centered . If a researcher is concerned to investigate the effects of a particular cause, with no preconceptions about what these effects might be, the research will be described as X‐centered . If a researcher is concerned to investigate a particular causal relationship, the research will be described as X   1 / Y‐centered , for it connects a particular cause with a particular outcome. 2   X ‐ or Y ‐centered research is exploratory; its purpose is to generate new hypotheses. X   1 / Y‐centered research, by contrast, is confirmatory/disconfirmatory; its purpose is to test an existing hypothesis.

1 Typical Case

In order for a focused case study to provide insight into a broader phenomenon it must be representative of a broader set of cases. It is in this context that one may speak of a typical‐case approach to case selection. The typical case exemplifies what is considered to be a typical set of values, given some general understanding of a phenomenon. By construction, the typical case is also a representative case.

Some typical cases serve an exploratory role. Here, the author chooses a case based upon a set of descriptive characteristics and then probes for causal relationships. Robert and Helen Lynd (1929/1956) selected a single city “to be as representative as possible of contemporary American life.” Specifically, they were looking for a city with

1) a temperate climate; 2) a sufficiently rapid rate of growth to ensure the presence of a plentiful assortment of the growing pains accompanying contemporary social change; 3) an industrial culture with modern, high‐speed machine production; 4) the absence of dominance of the city's industry by a single plant (i.e., not a one‐industry town); 5) a substantial local artistic life to balance its industrial activity …; and 6) the absence of any outstanding peculiarities or acute local problems which would mark the city off from the midchannel sort of American community. ( Lynd and Lynd 1929/1956 , quoted in Yin 2004 , 29–30)

After examining a number of options the Lynds decided that Muncie, Indiana, was more representative than, or at least as representative as, other midsized cities in America, thus qualifying as a typical case.

This is an inductive approach to case selection. Note that typicality may be understood according to the mean, median, or mode on a particular dimension; there may be multiple dimensions (as in the foregoing example); and each may be differently weighted (some dimensions may be more important than others). Where the selection criteria are multidimensional and a large sample of potential cases is in play, some form of factor analysis may be useful in identifying the most‐typical case(s).

However, the more common employment of the typical‐case method involves a causal model of some phenomenon of theoretical interest. Here, the researcher has identified a particular outcome ( Y ), and perhaps a specific X   1 / Y hypothesis, which she wishes to investigate. In order to do so, she looks for a typical example of that causal relationship. Intuitively, one imagines that a case selected according to the mean values of all parameters must be a typical case relative to some causal relationship. However, this is by no means assured.

Suppose that the Lynds were primarily interested in explaining feelings of trust/distrust among members of different social classes (one of the implicit research goals of the Middletown study). This outcome is likely to be affected by many factors, only some of which are included in their six selection criteria. So choosing cases with respect to a causal hypothesis involves, first of all, identifying the relevant parameters. It involves, secondly, the selection of a case that has a “typical” value relative to the overall causal model; it is well explained. Cases with untypical scores on a particular dimension (e.g. very high or very low) may still be typical examples of a causal relationship. Indeed, they may be more typical than cases whose values lie close to the mean. Thus, a descriptive understanding of typicality is quite different from a causal understanding of typicality. Since it is the latter version that is more common, I shall adopt this understanding of typicality in the remainder of the discussion.

From a qualitative perspective, causal typicality involves the selection of a case that conforms to expectations about some general causal relationship. It performs as expected. In a quantitative setting, this notion is measured by the size of a case's residual in a large‐ N cross‐case model. Typical cases lie on or near the regression line; their residuals are small. Insofar as the model is correctly specified, the size of a case's residual (i.e. the number of standard deviations that separate the actual value from the fitted value) provides a helpful clue to how representative that case is likely to be. “Outliers” are unlikely to be representative of the target population.

Of course, just because a case has a low residual does not necessarily mean that it is a representative case (with respect to the causal relationship of interest). Indeed, the issue of case representativeness is an issue that can never be definitively settled. When one refers to a “typical case” one is saying, in effect, that the probability of a case's representativeness is high, relative to other cases. This test of typicality is misleading if the statistical model is mis‐specified. And it provides little insurance against errors that are purely stochastic. A case may lie directly on the regression line but still be, in some important respect, atypical. For example, it might have an odd combination of values; the interaction of variables might be different from other cases; or additional causal mechanisms might be at work. For this reason, it is important to supplement a statistical analysis of cases with evidence drawn from the case in question (the case study itself) and with our deductive knowledge of the world. One should never judge a case solely by its residual. Yet, all other things being equal, a case with a low residual is less likely to be unusual than a case with a high residual, and to this extent the method of case selection outlined here may be a helpful guide to case‐study researchers faced with a large number of potential cases.

By way of conclusion, it should be noted that because the typical case embodies a typical value on some set of causally relevant dimensions, the variance of interest to the researcher must lie within that case. Specifically, the typical case of some phenomenon may be helpful in exploring causal mechanisms and in solving identification problems (e.g. endogeneity between X   1 and Y , an omitted variable that may account for X   1   and Y , or some other spurious causal association). Depending upon the results of the case study, the author may confirm an existing hypothesis, disconfirm that hypothesis, or reframe it in a way that is consistent with the findings of the case study. These are the uses of the typical‐case study.

2 Diverse Cases

A second case‐selection strategy has as its primary objective the achievement of maximum variance along relevant dimensions. I refer to this as a diverse‐case method. For obvious reasons, this method requires the selection of a set of cases—at minimum, two—which are intended to represent the full range of values characterizing X   1 , Y , or some particular X   1 / Y relationship. 3

Where the individual variable of interest is categorical (on/off, red/black/blue, Jewish/Protestant/Catholic), the identification of diversity is readily apparent. The investigator simply chooses one case from each category. For a continuous variable, the choices are not so obvious. However, the researcher usually chooses both extreme values (high and low), and perhaps the mean or median as well. The researcher may also look for break‐points in the distribution that seem to correspond to categorical differences among cases. Or she may follow a theoretical hunch about which threshold values count, i.e. which are likely to produce different values on Y .

Another sort of diverse case takes account of the values of multiple variables (i.e. a vector), rather than a single variable. If these variables are categorical, the identification of causal types rests upon the intersection of each category. Two dichotomous variables produce a matrix with four cells. Three trichotomous variables produce a matrix of eight cells. And so forth. If all variables are deemed relevant to the analysis, the selection of diverse cases mandates the selection of one case drawn from within each cell. Let us say that an outcome is thought to be affected by sex, race (black/white), and marital status. Here, a diverse‐case strategy of case selection would identify one case within each of these intersecting cells—a total of eight cases. Things become slightly more complicated when one or more of the factors is continuous, rather than categorical. Here, the diversity of case values do not fall neatly into cells. Rather, these cells must be created by fiat—e.g. high, medium, low.

It will be seen that where multiple variables are under consideration, the logic of diverse‐case analysis rests upon the logic of typological theorizing—where different combinations of variables are assumed to have effects on an outcome that vary across types ( Elman 2005 ; George and Bennett 2005 , 235; Lazarsfeld and Barton 1951 ). George and Smoke, for example, wish to explore different types of deterrence failure—by “fait accompli,” by “limited probe,” and by “controlled pressure.” Consequently, they wish to find cases that exemplify each type of causal mechanism. 4

Diversity may thus refer to a range of variation on X or Y , or to a particular combination of causal factors (with or without a consideration of the outcome). In each instance, the goal of case selection is to capture the full range of variation along the dimension(s) of interest.

Since diversity can mean many things, its employment in a large‐ N setting is necessarily dependent upon how this key term is defined. If it is understood to pertain only to a single variable ( X   1 or Y ), then the task is fairly simple. A categorical variable mandates the choice of at least one case from each category—two if dichotomous, three if trichotomous, and so forth. A continuous variable suggests the choice of at least one “high” and “low” value, and perhaps one drawn from the mean or median. But other choices might also be justified, according to one's hunch about the underlying causal relationship or according to natural thresholds found in the data, which may be grouped into discrete categories. Single‐variable traits are usually easy to discover in a large‐ N setting through descriptive statistics or through visual inspection of the data.

Where diversity refers to particular combinations of variables, the relevant cross‐ case technique is some version of stratified random sampling (in a probabilistic setting) or Qualitative Comparative Analysis (in a deterministic setting) ( Ragin 2000 ). If the researcher suspects that a causal relationship is affected not only by combinations of factors but also by their sequencing , then the technique of analysis must incorporate temporal elements ( Abbott 2001 ; Abbott and Forrest 1986 ; Abbott and Tsay 2000 ). Thus, the method of identifying causal types rests upon whatever method of identifying causal relationships is employed in the large‐ N sample.

Note that the identification of distinct case types is intended to identify groups of cases that are internally homogeneous (in all respects that might affect the causal relationship of interest). Thus, the choice of cases within each group should not be problematic, and may be accomplished through random sampling or purposive case selection. However, if there is suspected diversity within each category, then measures should be taken to assure that the chosen cases are typical of each category. A case study should not focus on an atypical member of a subgroup.

Indeed, considerations of diversity and typicality often go together. Thus, in a study of globalization and social welfare systems, Duane Swank (2002) first identifies three distinctive groups of welfare states: “universalistic” (social democratic), “corporatist conservative,” and “liberal.” Next, he looks within each group to find the most‐typical cases. He decides that the Nordic countries are more typical of the universalistic model than the Netherlands since the latter has “some characteristics of the occupationally based program structure and a political context of Christian Democratic‐led governments typical of the corporatist conservative nations” ( Swank 2002 , 11; see also Esping‐Andersen 1990 ). Thus, the Nordic countries are chosen as representative cases within the universalistic case type, and are accompanied in the case‐study portion of his analysis by other cases chosen to represent the other welfare state types (corporatist conservative and liberal).

Evidently, when a sample encompasses a full range of variation on relevant parameters one is likely to enhance the representativeness of that sample (relative to some population). This is a distinct advantage. Of course, the inclusion of a full range of variation may distort the actual distribution of cases across this spectrum. If there are more “high” cases than “low” cases in a population and the researcher chooses only one high case and one low case, the resulting sample of two is not perfectly representative. Even so, the diverse‐case method probably has stronger claims to representativeness than any other small‐ N sample (including the standalone typical case). The selection of diverse cases has the additional advantage of introducing variation on the key variables of interest. A set of diverse cases is, by definition, a set of cases that encompasses a range of high and low values on relevant dimensions. There is, therefore, much to recommend this method of case selection. I suspect that these advantages are commonly understood and are applied on an intuitive level by case‐study researchers. However, the lack of a recognizable name—and an explicit methodological defense—has made it difficult for case‐study researchers to utilize this method of case selection, and to do so in an explicit and self‐conscious fashion. Neologism has its uses.

3 Extreme Case

The extreme‐case method selects a case because of its extreme value on an independent ( X   1 ) or dependent ( Y ) variable of interest. Thus, studies of domestic violence may choose to focus on extreme instances of abuse ( Browne 1987 ). Studies of altruism may focus on those rare individuals who risked their lives to help others (e.g. Holocaust resisters) ( Monroe 1996 ). Studies of ethnic politics may focus on the most heterogeneous societies (e.g. Papua New Guinea) in order to better understand the role of ethnicity in a democratic setting ( Reilly 2000–1 ). Studies of industrial policy often focus on the most successful countries (i.e. the NICS) ( Deyo 1987 ). And so forth. 5

Often an extreme case corresponds to a case that is considered to be prototypical or paradigmatic of some phenomena of interest. This is because concepts are often defined by their extremes, i.e. their ideal types. Italian Fascism defines the concept of Fascism, in part, because it offered the most extreme example of that phenomenon. However, the methodological value of this case, and others like it, derives from its extremity (along some dimension of interest), not its theoretical status or its status in the literature on a subject.

The notion of “extreme” may now be defined more precisely. An extreme value is an observation that lies far away from the mean of a given distribution. This may be measured (if there are sufficient observations) by a case's “Z score”—the number of standard deviations between a case and the mean value for that sample. Extreme cases have high Z scores, and for this reason may serve as useful subjects for intensive analysis.

For a continuous variable, the distance from the mean may be in either direction (positive or negative). For a dichotomous variable (present/absent), extremeness may be interpreted as unusual . If most cases are positive along a given dimension, then a negative case constitutes an extreme case. If most cases are negative, then a positive case constitutes an extreme case. It should be clear that researchers are not simply concerned with cases where something “happened,” but also with cases where something did not. It is the rareness of the value that makes a case valuable, in this context, not its positive or negative value. 6 Thus, if one is studying state capacity, a case of state failure is probably more informative than a case of state endurance simply because the former is more unusual. Similarly, if one is interested in incest taboos a culture where the incest taboo is absent or weak is probably more useful than a culture where it is present or strong. Fascism is more important than nonfascism. And so forth. There is a good reason, therefore, why case studies of revolution tend to focus on “revolutionary” cases. Theda Skocpol (1979) had much more to learn from France than from Austro‐Hungary since France was more unusual than Austro‐Hungary within the population of nation states that Skocpol was concerned to explain. The reason is quite simple: There are fewer revolutionary cases than nonrevolutionary cases; thus, the variation that we explore as a clue to causal relationships is encapsulated in these cases, against a background of nonrevolutionary cases.

Note that the extreme‐case method of case selection appears to violate the social science folk wisdom warning us not to “select on the dependent variable.” 7 Selecting cases on the dependent variable is indeed problematic if a number of cases are chosen, all of which lie on one end of a variable's spectrum (they are all positive or negative), and if the researcher then subjects this sample to cross‐case analysis as if it were representative of a population. 8 Results for this sort of analysis would almost assuredly be biased. Moreover, there will be little variation to explain since the values of each case are explicitly constrained.

However, this is not the proper employment of the extreme‐case method. (It is more appropriately labeled an extreme‐ sample method.) The extreme‐case method actually refers back to a larger sample of cases that lie in the background of the analysis and provide a full range of variation as well as a more representative picture of the population. It is a self‐conscious attempt to maximize variance on the dimension of interest, not to minimize it. If this population of cases is well understood— either through the author's own cross‐case analysis, through the work of others, or through common sense—then a researcher may justify the selection of a single case exemplifying an extreme value for within‐case analysis. If not, the researcher may be well advised to follow a diverse‐case method, as discussed above.

By way of conclusion, let us return to the problem of representativeness. It will be seen that an extreme case may be typical or deviant. There is simply no way to tell because the researcher has not yet specified an X   1 / Y causal proposition. Once such a causal proposition has been specified one may then ask whether the case in question is similar to some population of cases in all respects that might affect the X   1 / Y relationship of interest (i.e. unit homogeneous). It is at this point that it becomes possible to say, within the context of a cross‐case statistical model, whether a case lies near to, or far from, the regression line. However, this sort of analysis means that the researcher is no longer pursuing an extreme‐case method. The extreme‐case method is purely exploratory—a way of probing possible causes of Y , or possible effects of X , in an open‐ended fashion. If the researcher has some notion of what additional factors might affect the outcome of interest, or of what relationship the causal factor of interest might have with Y , then she ought to pursue one of the other methods explored in this chapter. This also implies that an extreme‐case method may transform into a different kind of approach as a study evolves; that is, as a more specific hypothesis comes to light. Useful extreme cases at the outset of a study may prove less useful at a later stage of analysis.

4 Deviant Case

The deviant‐case method selects that case(s) which, by reference to some general understanding of a topic (either a specific theory or common sense), demonstrates a surprising value. It is thus the contrary of the typical case. Barbara Geddes (2003) notes the importance of deviant cases in medical science, where researchers are habitually focused on that which is “pathological” (according to standard theory and practice). The New England Journal of Medicine , one of the premier journals of the field, carries a regular feature entitled Case Records of the Massachusetts General Hospital. These articles bear titles like the following: “An 80‐Year‐Old Woman with Sudden Unilateral Blindness” or “A 76‐Year‐Old Man with Fever, Dyspnea, Pulmonary Infiltrates, Pleural Effusions, and Confusion.” 9 Another interesting example drawn from the field of medicine concerns the extensive study now devoted to a small number of persons who seem resistant to the AIDS virus ( Buchbinder and Vittinghoff 1999 ; Haynes, Pantaleo, and Fauci 1996 ). Why are they resistant? What is different about these people? What can we learn about AIDS in other patients by observing people who have built‐in resistance to this disease?

Likewise, in psychology and sociology case studies may be comprised of deviant (in the social sense) persons or groups. In economics, case studies may consist of countries or businesses that overperform (e.g. Botswana; Microsoft) or underperform (e.g. Britain through most of the twentieth century; Sears in recent decades) relative to some set of expectations. In political science, case studies may focus on countries where the welfare state is more developed (e.g. Sweden) or less developed (e.g. the United States) than one would expect, given a set of general expectations about welfare state development. The deviant case is closely linked to the investigation of theoretical anomalies. Indeed, to say deviant is to imply “anomalous.” 10

Note that while extreme cases are judged relative to the mean of a single distribution (the distribution of values along a single variable), deviant cases are judged relative to some general model of causal relations. The deviant‐case method selects cases which, by reference to some (presumably) general relationship, demonstrate a surprising value. They are “deviant” in that they are poorly explained by the multivariate model. The important point is that deviant‐ness can only be assessed relative to the general (quantitative or qualitative) model. This means that the relative deviant‐ness of a case is likely to change whenever the general model is altered. For example, the United States is a deviant welfare state when this outcome is gauged relative to societal wealth. But it is less deviant—and perhaps not deviant at all—when certain additional (political and societal) factors are included in the model, as discussed in the epilogue. Deviance is model dependent. Thus, when discussing the concept of the deviant case it is helpful to ask the following question: Relative to what general model (or set of background factors) is Case A deviant?

Conceptually, we have said that the deviant case is the logical contrary of the typical case. This translates into a directly contrasting statistical measurement. While the typical case is one with a low residual (in some general model of causal relations), a deviant case is one with a high residual. This means, following our previous discussion, that the deviant case is likely to be an un representative case, and in this respect appears to violate the supposition that case‐study samples should seek to reproduce features of a larger population.

However, it must be borne in mind that the primary purpose of a deviant‐case analysis is to probe for new—but as yet unspecified—explanations. (If the purpose is to disprove an extant theory I shall refer to the study as crucial‐case, as discussed below.) The researcher hopes that causal processes identified within the deviant case will illustrate some causal factor that is applicable to other (more or less deviant) cases. This means that a deviant‐case study usually culminates in a general proposition, one that may be applied to other cases in the population. Once this general proposition has been introduced into the overall model, the expectation is that the chosen case will no longer be an outlier. Indeed, the hope is that it will now be typical , as judged by its small residual in the adjusted model. (The exception would be a circumstance in which a case's outcome is deemed to be “accidental,” and therefore inexplicable by any general model.)

This feature of the deviant‐case study should help to resolve questions about its representativeness. Even if it is not possible to measure the new causal factor (and thus to introduce it into a large‐ N cross‐case model), it may still be plausible to assert (based on general knowledge of the phenomenon) that the chosen case is representative of a broader population.

5 Influential Case

Sometimes, the choice of a case is motivated solely by the need to verify the assumptions behind a general model of causal relations. Here, the analyst attempts to provide a rationale for disregarding a problematic case or a set of problematic cases. That is to say, she attempts to show why apparent deviations from the norm are not really deviant, or do not challenge the core of the theory, once the circumstances of the special case or cases are fully understood. A cross‐case analysis may, after all, be marred by several classes of problems including measurement error, specification error, errors in establishing proper boundaries for the inference (the scope of the argument), and stochastic error (fluctuations in the phenomenon under study that are treated as random, given available theoretical resources). If poorly fitting cases can be explained away by reference to these kinds of problems, then the theory of interest is that much stronger. This sort of deviant‐case analysis answers the question, “What about Case A (or cases of type A)? How does that, seemingly disconfirming, case fit the model?”

Because its underlying purpose is different from the usual deviant‐case study, I offer a new term for this method. The influential case is a case that casts doubt upon a theory, and for that reason warrants close inspection. This investigation may reveal, after all, that the theory is validated—perhaps in some slightly altered form. In this guise, the influential case is the “case that proves the rule.” In other instances, the influential‐case analysis may contribute to disconfirming, or reconceptualizing, a theory. The key point is that the value of the case is judged relative to some extant cross‐case model.

A simple version of influential‐case analysis involves the confirmation of a key case's score on some critical dimension. This is essentially a question of measurement. Sometimes cases are poorly explained simply because they are poorly understood. A close examination of a particular context may reveal that an apparently falsifying case has been miscoded. If so, the initial challenge presented by that case to some general theory has been obviated.

However, the more usual employment of the influential‐case method culminates in a substantive reinterpretation of the case—perhaps even of the general model. It is not just a question of measurement. Consider Thomas Ertman's (1997) study of state building in Western Europe, as summarized by Gerardo Munck. This study argues

that the interaction of a) the type of local government during the first period of statebuilding, with b) the timing of increases in geopolitical competition, strongly influences the kind of regime and state that emerge. [Ertman] tests this hypothesis against the historical experience of Europe and finds that most countries fit his predictions. Denmark, however, is a major exception. In Denmark, sustained geopolitical competition began relatively late and local government at the beginning of the statebuilding period was generally participatory, which should have led the country to develop “patrimonial constitutionalism.” But in fact, it developed “bureaucratic absolutism.” Ertman carefully explores the process through which Denmark came to have a bureaucratic absolutist state and finds that Denmark had the early marks of a patrimonial constitutionalist state. However, the country was pushed off this developmental path by the influence of German knights, who entered Denmark and brought with them German institutions of local government. Ertman then traces the causal process through which these imported institutions pushed Denmark to develop bureaucratic absolutism, concluding that this development was caused by a factor well outside his explanatory framework. ( Munck 2004 , 118)

Ertman's overall framework is confirmed insofar as he has been able to show, by an in‐depth discussion of Denmark, that the causal processes stipulated by the general theory hold even in this apparently disconfirming case. Denmark is still deviant, but it is so because of “contingent historical circumstances” that are exogenous to the theory ( Ertman 1997 , 316).

Evidently, the influential‐case analysis is similar to the deviant‐case analysis. Both focus on outliers. However, as we shall see, they focus on different kinds of outliers. Moreover, the animating goals of these two research designs are quite different. The influential‐case study begins with the aim of confirming a general model, while the deviant‐case study has the aim of generating a new hypothesis that modifies an existing general model. The confusion stems from the fact that the same case study may fulfill both objectives—qualifying a general model and, at the same time, confirming its core hypothesis.

Thus, in their study of Roberto Michels's “iron law of oligarchy,” Lipset, Trow, and Coleman (1956) choose to focus on an organization—the International Typographical Union—that appears to violate the central presupposition. The ITU, as noted by one of the authors, has “a long‐term two‐party system with free elections and frequent turnover in office” and is thus anything but oligarchic ( Lipset 1959 , 70). As such, it calls into question Michels's grand generalization about organizational behavior. The authors explain this curious result by the extraordinarily high level of education among the members of this union. Michels's law is shown to be true for most organizations, but not all. It is true, with qualifications. Note that the respecification of the original model (in effect, Lipset, Trow, and Coleman introduce a new control variable or boundary condition) involves the exploration of a new hypothesis. In this instance, therefore, the use of an influential case to confirm an existing theory is quite similar to the use of a deviant case to explore a new theory.

In a quantitative idiom, influential cases are those that, if counterfactually assigned a different value on the dependent variable, would most substantially change the resulting estimates. They may or may not be outliers (high‐residual cases). Two quantitative measures of influence are commonly applied in regression diagnostics ( Belsey, Kuh, and Welsch 2004 ). The first, often referred to as the leverage of a case, derives from what is called the hat matrix . Based solely on each case's scores on the independent variables, the hat matrix tells us how much a change in (or a measurement error on) the dependent variable for that case would affect the overall regression line. The second is Cook's distance , a measure of the extent to which the estimates of all the parameters would change if a given case were omitted from the analysis. Cases with a large leverage or Cook's distance contribute quite a lot to the inferences drawn from a cross‐case analysis. In this sense, such cases are vital for maintaining analytic conclusions. Discovering a significant measurement error on the dependent variable or an important omitted variable for such a case may dramatically revise estimates of the overall relationships. Hence, it may be quite sensible to select influential cases for in‐depth study.

Note that the use of an influential‐case strategy of case selection is limited to instances in which a researcher has reason to be concerned that her results are being driven by one or a few cases. This is most likely to be true in small to moderate‐sized samples. Where N is very large—greater than 1,000, let us say—it is extremely unlikely that a small set of cases (much less an individual case) will play an “influential” role. Of course, there may be influential sets of cases, e.g. countries within a particular continent or cultural region, or persons of Irish extraction. Sets of influential observations are often problematic in a time‐series cross‐section data‐set where each unit (e.g. country) contains multiple observations (through time), and hence may have a strong influence on aggregate results. Still, the general rule is: the larger the sample, the less important individual cases are likely to be and, hence, the less likely a researcher is to use an influential‐case approach to case selection.

6 Crucial Case

Of all the extant methods of case selection perhaps the most storied—and certainly the most controversial—is the crucial‐case method, introduced to the social science world several decades ago by Harry Eckstein. In his seminal essay, Eckstein (1975 , 118) describes the crucial case as one “that must closely fit a theory if one is to have confidence in the theory's validity, or, conversely, must not fit equally well any rule contrary to that proposed.” A case is crucial in a somewhat weaker—but much more common—sense when it is most, or least, likely to fulfill a theoretical prediction. A “most‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted to achieve a certain outcome, and yet does not. It is therefore used to disconfirm a theory. A “least‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted not to achieve a certain outcome, and yet does so. It is therefore used to confirm a theory. In all formulations, the crucial‐case offers a most‐difficult test for an argument, and hence provides what is perhaps the strongest sort of evidence possible in a nonexperimental, single‐case setting.

Since the publication of Eckstein's influential essay, the crucial‐case approach has been claimed in a multitude of studies across several social science disciplines and has come to be recognized as a staple of the case‐study method. 11 Yet the idea of any single case playing a crucial (or “critical”) role is not widely accepted among most methodologists (e.g. Sekhon 2004 ). (Even its progenitor seems to have had doubts.)

Let us begin with the confirmatory (a.k.a. least‐likely) crucial case. The implicit logic of this research design may be summarized as follows. Given a set of facts, we are asked to contemplate the probability that a given theory is true. While the facts matter, to be sure, the effectiveness of this sort of research also rests upon the formal properties of the theory in question. Specifically, the degree to which a theory is amenable to confirmation is contingent upon how many predictions can be derived from the theory and on how “risky” each individual prediction is. In Popper's (1963 , 36) words, “Confirmations should count only if they are the result of risky predictions ; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory—and event which would have refuted the theory. Every ‘good’ scientific theory is a prohibition; it forbids certain things to happen. The more a theory forbids, the better it is” (see also Popper 1934/1968 ). A risky prediction is therefore one that is highly precise and determinate, and therefore unlikely to be achieved by the product of other causal factors (external to the theory of interest) or through stochastic processes. A theory produces many such predictions if it is fully elaborated, issuing predictions not only on the central outcome of interest but also on specific causal mechanisms, and if it is broad in purview. (The notion of riskiness may also be conceptualized within the Popperian lexicon as degrees of falsifiability .)

These points can also be articulated in Bayesian terms. Colin Howson and Peter Urbach explain: “The degree to which h [a hypothesis] is confirmed by e [a set of evidence] depends … on the extent to which P(eČh) exceeds P (e) , that is, on how much more probable e is relative to the hypothesis and background assumptions than it is relative just to background assumptions.” Again, “confirmation is correlated with how much more probable the evidence is if the hypothesis is true than if it is false” ( Howson and Urlbach 1989 , 86). Thus, the stranger the prediction offered by a theory—relative to what we would normally expect—the greater the degree of confirmation that will be afforded by the evidence. As an intuitive example, Howson and Urbach (1989 , 86) offer the following:

If a soothsayer predicts that you will meet a dark stranger sometime and you do in fact, your faith in his powers of precognition would not be much enhanced: you would probably continue to think his predictions were just the result of guesswork. However, if the prediction also gave the correct number of hairs on the head of that stranger, your previous scepticism would no doubt be severely shaken.

While these Popperian/Bayesian notions 12 are relevant to all empirical research designs, they are especially relevant to case‐study research designs, for in these settings a single case (or, at most, a small number of cases) is required to bear a heavy burden of proof. It should be no surprise, therefore, that Popper's idea of “riskiness” was to be appropriated by case‐study researchers like Harry Eckstein to validate the enterprise of single‐case analysis. (Although Eckstein does not cite Popper the intellectual lineage is clear.) Riskiness, here, is analogous to what is usually referred to as a “most‐ difficult” research design, which in a case‐study research design would be understood as a “least‐likely” case. Note also that the distinction between a “must‐fit” case and a least‐likely case—that, in the event, actually does fit the terms of a theory—is a matter of degree. Cases are more or less crucial for confirming theories. The point is that, in some circumstances, a paucity of empirical evidence may be compensated by the riskiness of the theory.

The crucial‐case research design is, perforce, a highly deductive enterprise; much depends on the quality of the theory under investigation. It follows that the theories most amenable to crucial‐case analysis are those which are lawlike in their precision, degree of elaboration, consistency, and scope. The more a theory attains the status of a causal law, the easier it will be to confirm, or to disconfirm, with a single case. Indeed, risky predictions are common in natural science fields such as physics, which in turn served as the template for the deductive‐nomological (“covering‐law”) model of science that influenced Eckstein and others in the postwar decades (e.g. Hempel 1942 ).

A frequently cited example is the first important empirical demonstration of the theory of relativity, which took the form of a single‐event prediction on the occasion of the May 29, 1919, solar eclipse ( Eckstein 1975 ; Popper 1963 ). Stephen Van Evera (1997 , 66–7) describes the impact of this prediction on the validation of Einstein's theory.

Einstein's theory predicted that gravity would bend the path of light toward a gravity source by a specific amount. Hence it predicted that during a solar eclipse stars near the sun would appear displaced—stars actually behind the sun would appear next to it, and stars lying next to the sun would appear farther from it—and it predicted the amount of apparent displacement. No other theory made these predictions. The passage of this one single‐case‐study test brought the theory wide acceptance because the tested predictions were unique—there was no plausible competing explanation for the predicted result—hence the passed test was very strong.

The strength of this test is the extraordinary fit between the theory and a set of facts found in a single case, and the corresponding lack of fit between all other theories and this set of facts. Einstein offered an explanation of a particular set of anomalous findings that no other existing theory could make sense of. Of course, one must assume that there was no—or limited—measurement error. And one must assume that the phenomenon of interest is largely invariant; light does not bend differently at different times and places (except in ways that can be understood through the theory of relativity). And one must assume, finally, that the theory itself makes sense on other grounds (other than the case of special interest); it is a plausible general theory. If one is willing to accept these a priori assumptions, then the 1919 “case study” provides a very strong confirmation of the theory. It is difficult to imagine a stronger proof of the theory from within an observational (nonexperimental) setting.

In social science settings, by contrast, one does not commonly find single‐case studies offering knockout evidence for a theory. This is, in my view, largely a product of the looseness (the underspecification) of most social science theories. George and Bennett point out that while the thesis of the democratic peace is as close to a “law” as social science has yet seen, it cannot be confirmed (or refuted) by looking at specific causal mechanisms because the causal pathways mandated by the theory are multiple and diverse. Under the circumstances, no single‐case test can offer strong confirmation of the theory ( George and Bennett 2005 , 209).

However, if one adopts a softer version of the crucial‐case method—the least‐likely (most difficult) case—then possibilities abound. Indeed, I suspect that, implicitly , most case‐study work that makes a positive argument focusing on a single case (without a corresponding cross‐case analysis) relies largely on the logic of the least‐ likely case. Rarely is this logic made explicit, except perhaps in a passing phrase or two. Yet the deductive logic of the “risky” prediction is central to the case‐study enterprise. Whether a case study is convincing or not often rests on the reader's evaluation of how strong the evidence for an argument might be, and this in turn—wherever cross‐ case evidence is limited and no manipulated treatment can be devised—rests upon an estimation of the degree of “fit” between a theory and the evidence at hand, as discussed.

Lily Tsai's (2007) investigation of governance at the village level in China employs several in‐depth case studies of villages which are chosen (in part) because of their least‐likely status relative to the theory of interest. Tsai's hypothesis is that villages with greater social solidarity (based on preexisting religious or familial networks) will develop a higher level of social trust and mutual obligation and, as a result, will experience better governance. Crucial cases, therefore, are villages that evidence a high level of social solidarity but which, along other dimensions, would be judged least likely to develop good governance, e.g. they are poor, isolated, and lack democratic institutions or accountability mechanisms from above. “Li Settlement,” in Fujian province, is such a case. The fact that this impoverished village nonetheless boasts an impressive set of infrastructural accomplishments such as paved roads with drainage ditches (a rarity in rural China) suggests that something rather unusual is going on here. Because her case is carefully chosen to eliminate rival explanations, Tsai's conclusions about the special role of social solidarity are difficult to gainsay. How else is one to explain this otherwise anomalous result? This is the strength of the least‐likely case, where all other plausible causal factors for an outcome have been minimized. 13

Jack Levy (2002 , 144) refers to this, evocatively, as a “Sinatra inference:” if it can make it here, it can make it anywhere (see also Khong 1992 , 49; Sagan 1995 , 49; Shafer 1988 , 14–6). Thus, if social solidarity has the hypothesized effect in Li Settlement it should have the same effect in more propitious settings (e.g. where there is greater economic surplus). The same implicit logic informs many case‐study analyses where the intent of the study is to confirm a hypothesis on the basis of a single case.

Another sort of crucial case is employed for the purpose of dis confirming a causal hypothesis. A central Popperian insight is that it is easier to disconfirm an inference than to confirm that same inference. (Indeed, Popper doubted that any inference could be fully confirmed, and for this reason preferred the term “corroborate.”) This is particularly true of case‐study research designs, where evidence is limited to one or several cases. The key proviso is that the theory under investigation must take a consistent (a.k.a. invariant, deterministic) form, even if its predictions are not terrifically precise, well elaborated, or broad.

As it happens, there are a fair number of invariant propositions floating around the social science disciplines (Goertz and Levy forthcoming; Goertz and Starr 2003 ). It used to be argued, for example, that political stability would occur only in countries that are relatively homogeneous, or where existing heterogeneities are mitigated by cross‐cutting cleavages ( Almond 1956 ; Bentley 1908/1967 ; Lipset 1960/1963 ; Truman 1951 ). Arend Lijphart's (1968) study of the Netherlands, a peaceful country with reinforcing social cleavages, is commonly viewed as refuting this theory on the basis of a single in‐depth case analysis. 14

Granted, it may be questioned whether presumed invariant theories are really invariant; perhaps they are better understood as probabilistic. Perhaps, that is, the theory of cross‐cutting cleavages is still true, probabilistically, despite the apparent Dutch exception. Or perhaps the theory is still true, deterministically, within a subset of cases that does not include the Netherlands. (This sort of claim seems unlikely in this particular instance, but it is quite plausible in many others.) Or perhaps the theory is in need of reframing; it is true, deterministically, but applies only to cross‐ cutting ethnic/racial cleavages, not to cleavages that are primarily religious. One can quibble over what it means to “disconfirm” a theory. The point is that the crucial case has, in all these circumstances, provided important updating of a theoretical prior.

Heretofore, I have treated causal factors as dichotomous. Countries have either reinforcing or cross‐cutting cleavages and they have regimes that are either peaceful or conflictual. Evidently, these sorts of parameters are often matters of degree. In this reading of the theory, cases are more or less crucial. Accordingly, the most useful—i.e. most crucial—case for Lijphart's purpose is one that has the most segregated social groups and the most peaceful and democratic track record. In these respects, the Netherlands was a very good choice. Indeed, the degree of disconfirmation offered by this case study is probably greater than the degree of disconfirmation that might have been provided by other cases such as India or Papua New Guinea—countries where social peace has not always been secure. The point is that where variables are continuous rather than dichotomous it is possible to evaluate potential cases in terms of their degree of crucialness .

Note that the crucial‐case method of case‐selection, whether employed in a confirmatory or disconfirmatory mode, cannot be employed in a large‐ N context. This is because an explicit cross‐case model would render the crucial‐case study redundant. Once one identifies the relevant parameters and the scores of all cases on those parameters, one has in effect constructed a cross‐case model that confirms or disconfirms the theory in question. The case study is thenceforth irrelevant, at least as a means of decisive confirmation or disconfirmation. 15 It remains highly relevant as a means of exploring causal mechanisms, of course. Yet, because this objective is quite different from that which is usually associated with the term, I enlist a new term for this technique.

7 Pathway Case

One of the most important functions of case‐study research is the elucidation of causal mechanisms. But which sort of case is most useful for this purpose? Although all case studies presumably shed light on causal mechanisms, not all cases are equally transparent. In situations where a causal hypothesis is clear and has already been confirmed by cross‐case analysis, researchers are well advised to focus on a case where the causal effect of X   1 on Y can be isolated from other potentially confounding factors ( X   2 ). I shall call this a pathway case to indicate its uniquely penetrating insight into causal mechanisms. In contrast to the crucial case, this sort of method is practicable only in circumstances where cross‐case covariational patterns are well studied and where the mechanism linking X   1 and Y remains dim. Because the pathway case builds on prior cross‐case analysis, the problem of case selection must be situated within that sample. There is no standalone pathway case.

The logic of the pathway case is clearest in situations of causal sufficiency—where a causal factor of interest, X   1 , is sufficient by itself (though perhaps not necessary) to account for Y 's value (0 or 1). The other causes of Y , about which we need make no assumptions, are designated as a vector, X   2 .

Note that wherever various causal factors are substitutable for one another, each factor is conceptualized (individually) as sufficient ( Braumoeller 2003 ). Thus, situations of causal equifinality presume causal sufficiency on the part of each factor or set of conjoint factors. An example is provided by the literature on democratization, which stipulates three main avenues of regime change: leadership‐initiated reform, a controlled opening to opposition, or the collapse of an authoritarian regime ( Colomer 1991 ). The case‐study format constrains us to analyze one at a time, so let us limit our scope to the first one—leadership‐initiated reform. So considered, a causal‐pathway case would be one with the following features: (a) democratization, (b) leadership‐initiated reform, (c) no controlled opening to the opposition, (d) no collapse of the previous authoritarian regime, and (e) no other extraneous factors that might affect the process of democratization. In a case of this type, the causal mechanisms by which leadership‐initiated reform may lead to democratization will be easiest to study. Note that it is not necessary to assume that leadership‐initiated reform always leads to democratization; it may or may not be a deterministic cause. But it is necessary to assume that leadership‐initiated reform can sometimes lead to democratization on its own (given certain background features).

Now let us move from these examples to a general‐purpose model. For heuristic purposes, let us presume that all variables in that model are dichotomous (coded as 0 or 1) and that the model is complete (all causes of Y are included). All causal relationships will be coded so as to be positive: X   1 and Y covary as do X   2 and Y . This allows us to visualize a range of possible combinations at a glance.

Recall that the pathway case is always focused, by definition, on a single causal factor, denoted X   1 . (The researcher's focus may shift to other causal factors, but may only focus on one causal factor at a time.) In this scenario, and regardless of how many additional causes of Y there might be (denoted X   2 , a vector of controls), there are only eight relevant case types, as illustrated in Table 28.2 . Identifying these case types is a relatively simple matter, and can be accomplished in a small‐ N sample by the construction of a truth‐table (modeled after Table 28.2 ) or in a large‐ N sample by the use of cross‐tabs.

Notes : X   1 = the variable of theoretical interest. X   2 = a vector of controls (a score of 0 indicates that all control variables have a score of 0, while a score of 1 indicates that all control variables have a score of 1). Y = the outcome of interest. A–H = case types (the N for each case type is indeterminate). G, H = possible pathway cases. Sample size = indeterminate.

Assumptions : (a) all variables can be coded dichotomously (a binary coding of the concept is valid); (b) all independent variables are positively correlated with Y in the general case; ( c ) X   1 is (at least sometimes) a sufficient cause of Y .

Note that the total number of combinations of values depends on the number of control variables, which we have represented with a single vector, X   2 . If this vector consists of a single variable then there are only eight case types. If this vector consists of two variables ( X   2a , X   2b ) then the total number of possible combinations increases from eight (2 3 ) to sixteen (2 4 ). And so forth. However, none of these combinations is relevant for present purposes except those where X   2a and X   2b have the same value (0 or 1). “Mixed” cases are not causal pathway cases, for reasons that should become clear.

The pathway case, following the logic of the crucial case, is one where the causal factor of interest, X   1 , correctly predicts Y while all other possible causes of Y (represented by the vector, X   2 ) make “wrong” predictions. If X   1 is—at least in some circumstances—a sufficient cause of Y , then it is these sorts of cases that should be most useful for tracing causal mechanisms. There are only two such cases in Ta b l e 28.2—G and H. In all other cases, the mechanism running from X   1 to Y would be difficult to discern either because X   1 and Y are not correlated in the usual way (constituting an unusual case, in the terms of our hypothesis) or because other confounding factors ( X   2 ) intrude. In case A, for example, the positive value on Y could be a product of X   1 or X   2 . An in‐depth examination of this case is not likely to be very revealing.

Keep in mind that because the researcher already knows from her cross‐case examination what the general causal relationships are, she knows (prior to the case‐ study investigation) what constitutes a correct or incorrect prediction. In the crucial‐ case method, by contrast, these expectations are deductive rather than empirical. This is what differentiates the two methods. And this is why the causal pathway case is useful principally for elucidating causal mechanisms rather than verifying or falsifying general propositions (which are already more or less apparent from the cross‐case evidence). Of course, we must leave open the possibility that the investigation of causal mechanisms would invalidate a general claim, if that claim is utterly contingent upon a specific set of causal mechanisms and the case study shows that no such mechanisms are present. However, this is rather unlikely in most social science settings. Usually, the result of such a finding will be a reformulation of the causal processes by which X   1 causes Y —or, alternatively, a realization that the case under investigation is aberrant (atypical of the general population of cases).

Sometimes, the research question is framed as a unidirectional cause: one is interested in why 0 becomes 1 (or vice versa) but not in why 1 becomes 0. In our previous example, we asked why democracies fail, not why countries become democratic or authoritarian. So framed, there can be only one type of causal‐pathway case. (Whether regime failure is coded as 0 or 1 is a matter of taste.) Where researchers are interested in bidirectional causality—a movement from 0 to 1 as well as from 1 to 0—there are two possible causal‐pathway cases, G and H. In practice, however, one of these case types is almost always more useful than the other. Thus, it seems reasonable to employ the term “pathway case” in the singular. In order to determine which of these two case types will be more useful for intensive analysis the researcher should look to see whether each case type exhibits desirable features such as: (a) a rare (unusual) value on X   1 or Y (designated “extreme” in our previous discussion), (b) observable temporal variation in X   1 , ( c ) an X   1 / Y relationship that is easier to study (it has more visible features; it is more transparent), or (d) a lower residual (thus indicating a more typical case, within the terms of the general model). Usually, the choice between G and H is intuitively obvious.

Now, let us consider a scenario in which all (or most) variables of concern to the model are continuous, rather than dichotomous. Here, the job of case selection is considerably more complex, for causal “sufficiency” (in the usual sense) cannot be invoked. It is no longer plausible to assume that a given cause can be entirely partitioned, i.e. rival factors eliminated. However, the search for a pathway case may still be viable. What we are looking for in this scenario is a case that satisfies two criteria: (1) it is not an outlier (or at least not an extreme outlier) in the general model and (2) its score on the outcome ( Y ) is strongly influenced by the theoretical variable of interest ( X   1 ), taking all other factors into account ( X   2 ). In this sort of case it should be easiest to “see” the causal mechanisms that lie between X   1 and Y .

Achieving the second desiderata requires a bit of manipulation. In order to determine which (nonoutlier) cases are most strongly affected by X   1 , given all the other parameters in the model, one must compare the size of the residuals for each case in a reduced form model, Y = Constant + X   2 + Res reduced , with the size of the residuals for each case in a full model, Y = Constant + X   2 + X   1 + Res full . The pathway case is that case, or set of cases, which shows the greatest difference between the residual for the reduced‐form model and the full model (ΔResidual). Thus,

Note that the residual for a case must be smaller in the full model than in the reduced‐ form model; otherwise, the addition of the variable of interest ( X   1 ) pulls the case away from the regression line. We want to find a case where the addition of X   1 pushes the case towards the regression line, i.e. it helps to “explain” that case.

As an example, let us suppose that we are interested in exploring the effect of mineral wealth on the prospects for democracy in a society. According to a good deal of work on this subject, countries with a bounty of natural resources—particularly oil—are less likely to democratize (or once having undergone a democratic transition, are more likely to revert to authoritarian rule) ( Barro 1999 ; Humphreys 2005 ; Ross 2001 ). The cross‐country evidence is robust. Yet as is often the case, the causal mechanisms remain rather obscure. In order to better understand this phenomenon it may be worthwhile to exploit the findings of cross‐country regression models in order to identify a country whose regime type (i.e. its democracy “score” on some general index) is strongly affected by its natural‐research wealth, all other things held constant. An analysis of this sort identifies two countries— the United Arab Emirates and Kuwait—with high Δ Residual values and modest residuals in the full model (signifying that these cases are not outliers). Researchers seeking to explore the effect of oil wealth on regime type might do well to focus on these two cases since their patterns of democracy cannot be well explained by other factors—e.g. economic development, religion, European influence, or ethnic fractionalization. The presence of oil wealth in these countries would appear to have a strong independent effect on the prospects for democratization in these cases, an effect that is well modeled by general theory and by the available cross‐case evidence.

To reiterate, the logic of causal “elimination” is much more compelling where variables are dichotomous and where causal sufficiency can be assumed ( X   1 is sufficient by itself, at least in some circumstances, to cause Y ). Where variables are continuous, the strategy of the pathway case is more dubious, for potentially confounding causal factors ( X   2 ) cannot be neatly partitioned. Even so, we have indicated why the selection of a pathway case may be a logical approach to case‐study analysis in many circumstances.

The exceptions may be briefly noted. Sometimes, where all variables in a model are dichotomous, there are no pathway cases, i.e. no cases of type G or H (in Table 28.2 ). This is known as the “empty cell” problem, or a problem of severe causal multicollinearity. The universe of observational data does not always oblige us with cases that allow us to independently test a given hypothesis. Where variables are continuous, the analogous problem is that of a causal variable of interest ( X   1 ) that has only minimal effects on the outcome of interest. That is, its role in the general model is quite minor. In these situations, the only cases that are strongly affected by X   1 —if there are any at all—may be extreme outliers, and these sorts of cases are not properly regarded as providing confirmatory evidence for a proposition, for reasons that are abundantly clear by now.

Finally, it should be clarified that the identification of a causal pathway case does not obviate the utility of exploring other cases. One might, for example, want to compare both sorts of potential pathway cases—G and H—with each other. Many other combinations suggest themselves. However, this sort of multi‐case investigation moves beyond the logic of the causal‐pathway case.

8 Most‐similar Cases

The most‐similar method employs a minimum of two cases. 16 In its purest form, the chosen pair of cases is similar in all respects except the variable(s) of interest. If the study is exploratory (i.e. hypothesis generating), the researcher looks for cases that differ on the outcome of theoretical interest but are similar on various factors that might have contributed to that outcome, as illustrated in Table 28.3 (A) . This is a common form of case selection at the initial stage of research. Often, fruitful analysis begins with an apparent anomaly: two cases are apparently quite similar, and yet demonstrate surprisingly different outcomes. The hope is that intensive study of these cases will reveal one—or at most several—factors that differ across these cases. These differing factors ( X   1 ) are looked upon as putative causes. At this stage, the research may be described by the second diagram in Table 28.3 (B) . Sometimes, a researcher begins with a strong hypothesis, in which case her research design is confirmatory (hypothesis testing) from the get‐go. That is, she strives to identify cases that exhibit different outcomes, different scores on the factor of interest, and similar scores on all other possible causal factors, as illustrated in the second (hypothesis‐testing) diagram in Table 28.3 (B) .

The point is that the purpose of a most‐similar research design, and hence its basic setup, often changes as a researcher moves from an exploratory to a confirmatory mode of analysis. However, regardless of where one begins, the results, when published, look like a hypothesis‐testing research design. Question marks have been removed: (A) becomes (B) in Table 28.3 .

As an example, let us consider Leon Epstein's classic study of party cohesion, which focuses on two “most‐similar” countries, the United States and Canada. Canada has highly disciplined parties whose members vote together on the floor of the House of Commons while the United States has weak, undisciplined parties, whose members often defect on floor votes in Congress. In explaining these divergent outcomes, persistent over many years, Epstein first discusses possible causal factors that are held more or less constant across the two cases. Both the United States and Canada inherited English political cultures, both have large territories and heterogeneous populations, both are federal, and both have fairly loose party structures with strong regional bases and a weak center. These are the “control” variables. Where they differ is in one constitutional feature: Canada is parliamentary while the United States is presidential. And it is this institutional difference that Epstein identifies as the crucial (differentiating) cause. (For further examples of the most‐similar method see Brenner 1976 ; Hamilton 1977 ; Lipset 1968 ; Miguel 2004 ; Moulder 1977 ; Posner 2004 .)

X   1 = the variable of theoretical interest. X   2 = a vector of controls. Y = the outcome of interest.

Several caveats apply to any most‐similar analysis (in addition to the usual set of assumptions applying to all case‐study analysis). First, each causal factor is understood as having an independent and additive effect on the outcome; there are no “interaction” effects. Second, one must code cases dichotomously (high/low, present/absent). This is straightforward if the underlying variables are also dichotomous (e.g. federal/unitary). However, it is often the case that variables of concern in the model are continuous (e.g. party cohesion). In this setting, the researcher must “dichotomize” the scoring of cases so as to simplify the two‐case analysis. (Some flexibility is admissible on the vector of controls ( X   2 ) that are “held constant” across the cases. Nonidentity is tolerable if the deviation runs counter to the predicted hypothesis. For example, Epstein describes both the United States and Canada as having strong regional bases of power, a factor that is probably more significant in recent Canadian history than in recent American history. However, because regional bases of power should lead to weaker parties, rather than stronger parties, this element of nonidentity does not challenge Epstein's conclusions. Indeed, it sets up a most‐difficult research scenario, as discussed above.)

In one respect the requirements for case control are not so stringent. Specifically, it is not usually necessary to measure control variables (at least not with a high degree of precision) in order to control for them. If two countries can be assumed to have similar cultural heritages one needn't worry about constructing variables to measure that heritage. One can simply assert that, whatever they are, they are more or less constant across the two cases. This is similar to the technique employed in a randomized experiment, where the researcher typically does not attempt to measure all the factors that might affect the causal relationship of interest. She assumes, rather, that these unknown factors have been neutralized across the treatment and control groups by randomization or by the choice of a sample that is internally homogeneous.

The most useful statistical tool for identifying cases for in‐depth analysis in a most‐ similar setting is probably some variety of matching strategy—e.g. exact matching, approximate matching, or propensity‐score matching. 17 The product of this procedure is a set of matched cases that can be compared in whatever way the researcher deems appropriate. These are the “most‐similar” cases. Rosenbaum and Silber (2001 , 223) summarize:

Unlike model‐based adjustments, where [individuals] vanish and are replaced by the coefficients of a model, in matching, ostensibly comparable patterns are compared directly, one by one. Modern matching methods involve statistical modeling and combinatorial algorithms, but the end result is a collection of pairs or sets of people who look comparable, at least on average. In matching, people retain their integrity as people, so they can be examined and their stories can be told individually.

Matching, conclude the authors, “facilitates, rather than inhibits, thick description” ( Rosenbaum and Silber 2001 , 223).

In principle, the same matching techniques that have been used successfully in observational studies of medical treatments might also be adapted to the study of nation states, political parties, cities, or indeed any traditional paired cases in the social sciences. Indeed, the current popularity of matching among statisticians—relative, that is, to garden‐variety regression models—rests upon what qualitative researchers would recognize as a “case‐based” approach to causal analysis. If Rosenbaum and Silber are correct, it may be perfectly reasonable to appropriate this large‐ N method of analysis for case‐study purposes.

As with other methods of case selection, the most‐similar method is prone to problems of nonrepresentativeness. If employed in a qualitative fashion (without a systematic cross‐case selection strategy), potential biases in the chosen case must be addressed in a speculative way. If the researcher employs a matching technique of case selection within a large‐ N sample, the problem of potential bias can be addressed by assuring the choice of cases that are not extreme outliers, as judged by their residuals in the full model. Most‐similar cases should also be “typical” cases, though some scope for deviance around the regression line may be acceptable for purposes of finding a good fit among cases.

X   1 = the variable of theoretical interest. X   2a–d = a vector of controls. Y = the outcome of interest.

9 Most‐different Cases

A final case‐selection method is the reverse image of the previous method. Here, variation on independent variables is prized, while variation on the outcome is eschewed. Rather than looking for cases that are most‐similar, one looks for cases that are most‐ different . Specifically, the researcher tries to identify cases where just one independent variable ( X   1 ), as well as the dependent variable ( Y ), covary, while all other plausible factors ( X   2a–d ) show different values. 18

The simplest form of this two‐case comparison is illustrated in Table 28.4 . Cases A and B are deemed “most different,” though they are similar in two essential respects— the causal variable of interest and the outcome.

As an example, I follow Marc Howard's (2003) recent work, which explores the enduring impact of Communism on civil society. 19 Cross‐national surveys show a strong correlation between former Communist regimes and low social capital, controlling for a variety of possible confounders. It is a strong result. Howard wonders why this relationship is so strong and why it persists, and perhaps even strengthens, in countries that are no longer socialist or authoritarian. In order to answer this question, he focuses on two most‐different cases, Russia and East Germany. These two countries were quite different—in all ways other than their Communist experience— prior to the Soviet era, during the Soviet era (since East Germany received substantial subsidies from West Germany), and in the post‐Soviet era, as East Germany was absorbed into West Germany. Yet, they both score near the bottom of various cross‐ national indices intended to measure the prevalence of civic engagement in the current era. Thus, Howard's (2003 , 6–9) case selection procedure meets the requirements of the most‐different research design: Variance is found on all (or most) dimensions aside from the key factor of interest (Communism) and the outcome (civic engagement).

What leverage is brought to the analysis from this approach? Howard's case studies combine evidence drawn from mass surveys and from in‐depth interviews of small, stratified samples of Russians and East Germans. (This is a good illustration, incidentally, of how quantitative and qualitative evidence can be fruitfully combined in the intensive study of several cases.) The product of this analysis is the identification of three causal pathways that, Howard (2003 , 122) claims, help to explain the laggard status of civil society in post‐Communist polities: “the mistrust of communist organizations, the persistence of friendship networks, and the disappointment with post‐communism.” Simply put, Howard (2003 , 145) concludes, “a great number of citizens in Russia and Eastern Germany feel a strong and lingering sense of distrust of any kind of public organization, a general satisfaction with their own personal networks (accompanied by a sense of deteriorating relations within society overall), and disappointment in the developments of post‐communism.”

The strength of this most‐different case analysis is that the results obtained in East Germany and Russia should also apply in other post‐Communist polities (e.g. Lithuania, Poland, Bulgaria, Albania). By choosing a heterogeneous sample, Howard solves the problem of representativeness in his restricted sample. However, this sample is demonstrably not representative across the population of the inference, which is intended to cover all countries of the world.

More problematic is the lack of variation on key causal factors of interest— Communism and its putative causal pathways. For this reason, it is difficult to reach conclusions about the causal status of these factors on the basis of the most‐different analysis alone. It is possible, that is, that the three causal pathways identified by Howard also operate within polities that never experienced Communist rule.

Nor does it seem possible to conclusively eliminate rival hypotheses on the basis of this most‐different analysis. Indeed, this is not Howard's intention. He wishes merely to show that whatever influence on civil society might be attributed to economic, cultural, and other factors does not exhaust this subject.

My considered judgment is that the most‐different research design provides minimal leverage into the problem of why Communist systems appear to suppress civic engagement, years after their disappearance. Fortunately, this is not the only research design employed by Howard in his admirable study. Indeed, the author employs two other small‐ N cross‐case methods, as well as a large‐ N cross‐country statistical analysis. These methods do most of the analytic work. East Germany may be regarded as a causal pathway case (see above). It has all the attributes normally assumed to foster civic engagement (e.g. a growing economy, multiparty competition, civil liberties, a free press, close association with Western European culture and politics), but nonetheless shows little or no improvement on this dimension during the post‐ transition era ( Howard 2003 , 8). It is plausible to attribute this lack of change to its Communist past, as Howard does, in which case East Germany should be a fruitful case for the investigation of causal mechanisms. The contrast between East and West Germany provides a most‐similar analysis since the two polities share virtually everything except a Communist past. This variation is also deftly exploited by Howard.

I do not wish to dismiss the most‐different research method entirely. Surely, Howard's findings are stronger with the intensive analysis of Russia than they would be without. Yet his book would not stand securely on the empirical foundation provided by most‐different analysis alone. If one strips away the pathway‐case (East Germany) and the most‐similar analysis (East/West Germany) there is little left upon which to base an analysis of causal relations (aside from the large‐ N cross‐national analysis). Indeed, most scholars who employ the most‐different method do so in conjunction with other methods. 20 It is rarely, if ever, a standalone method. 21

Generalizing from this discussion of Marc Howard's work, I offer the following summary remarks on the most‐different method of case analysis. (I leave aside issues faced by all case‐study analyses, issues that are explored in Gerring 2007 .)

Let us begin with a methodological obstacle that is faced by both Millean styles of analysis—the necessity of dichotomizing every variable in the analysis. Recall that, as with most‐similar analysis, differences across cases must generally be sizeable enough to be interpretable in an essentially dichotomous fashion (e.g. high/low, present/absent) and similarities must be close enough to be understood as essentially identical (e.g. high/high, present/present). Otherwise the results of a Millean style analysis are not interpretable. The problem of “degrees” is deadly if the variables under consideration are, by nature, continuous (e.g. GDP). This is a particular concern in Howard's analysis, where East Germany scores somewhat higher than Russia in civic engagement; they are both low, but Russia is quite a bit lower. Howard assumes that this divergence is minimal enough to be understood as a difference of degrees rather than of kinds, a judgment that might be questioned. In these respects, most‐different analysis is no more secure—but also no less—than most‐similar analysis.

In one respect, most‐different analysis is superior to most‐similar analysis. If the coding assumptions are sound, the most‐different research design may be quite useful for eliminating necessary causes . Causal factors that do not appear across the chosen cases—e.g. X   2a–d in Table 28.4 —are evidently unnecessary for the production of Y . However, it does not follow that the most‐different method is the best method for eliminating necessary causes. Note that the defining feature of this method is the shared element across cases— X   1 in Table 28.4 . This feature does not help one to eliminate necessary causes. Indeed, if one were focused solely on eliminating necessary causes one would presumably seek out cases that register the same outcomes and have maximum diversity on other attributes. In Table 28.4 , this would be a set of cases that satisfy conditions X   2a–d , but not X   1 . Thus, even the presumed strength of the most‐different analysis is not so strong.

Usually, case‐study analysis is focused on the identification (or clarification) of causal relations, not the elimination of possible causes. In this setting, the most‐ different technique is useful, but only if assumptions of causal uniqueness hold. By “causal uniqueness,” I mean a situation in which a given outcome is the product of only one cause: Y cannot occur except in the presence of X . X is necessary, and in some situations (given certain background conditions) sufficient, to cause Y . 22

Consider the following hypothetical example. Suppose that a new disease, about which little is known, has appeared in Country A. There are hundreds of infected persons across dozens of affected communities in that country. In Country B, located at the other end of the world, several new cases of the disease surface in a single community. In this setting, we can imagine two sorts of Millean analyses. The first examines two similar communities within Country A, one of which has developed the disease and the other of which has not. This is the most‐similar style of case comparison, and focuses accordingly on the identification of a difference between the two cases that might account for variation across the sample. A second approach focuses on communities where the disease has appeared across the two countries and searches for any similarities that might account for these similar outcomes. This is the most‐different research design.

Both are plausible approaches to this particular problem, and we can imagine epidemiologists employing them simultaneously. However, the most‐different design demands stronger assumptions about the underlying factors at work. It supposes that the disease arises from the same cause in any setting. This is often a reasonable operating assumption when one is dealing with natural phenomena, though there are certainly many exceptions. Death, for example, has many causes. For this reason, it would not occur to us to look for most‐different cases of high mortality around the world. In order for the most‐different research design to effectively identify a causal factor at work in a given outcome, the researcher must assume that X   1 —the factor held constant across the diverse cases—is the only possible cause of Y (see Table 28.4 ). This assumption rarely holds in social‐scientific settings. Most outcomes of interest to anthropologists, economists, political scientists, and sociologists have multiple causes. There are many ways to win an election, to build a welfare state, to get into a war, to overthrow a government, or—returning to Marc Howard's work—to build a strong civil society. And it is for this reason that most‐different analysis is rarely applied in social science work and, where applied, is rarely convincing.

If this seems a tad severe, there is a more charitable way of approaching the most‐different method. Arguably, this is not a pure “method” at all but merely a supplement, a way of incorporating diversity in the sub‐sample of cases that provide the unusual outcome of interest. If the unusual outcome is revolutions, one might wish to encompass a wide variety of revolutions in one's analysis. If the unusual outcome is post‐Communist civil society, it seems appropriate to include a diverse set of post‐Communist polities in one's sample of case studies, as Marc Howard does. From this perspective, the most‐different method (so‐called) might be better labeled a diverse‐case method, as explored above.

10 Conclusions

In order to be a case of something broader than itself, the chosen case must be representative (in some respects) of a larger population. Otherwise—if it is purely idiosyncratic (“unique”)—it is uninformative about anything lying outside the borders of the case itself. A study based on a nonrepresentative sample has no (or very little) external validity. To be sure, no phenomenon is purely idiosyncratic; the notion of a unique case is a matter that would be difficult to define. One is concerned, as always, with matters of degree. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. (The one exception, as noted, is the influential case.)

Of all the problems besetting case‐study analysis, perhaps the most persistent— and the most persistently bemoaned—is the problem of sample bias ( Achen and Snidal 1989 ; Collier and Mahoney 1996 ; Geddes 1990 ; King, Keohane, and Verba 1994 ; Rohlfing 2004 ; Sekhon 2004 ). Lisa Martin (1992 , 5) finds that the overemphasis of international relations scholars on a few well‐known cases of economic sanctions— most of which failed to elicit any change in the sanctioned country—“has distorted analysts view of the dynamics and characteristics of economic sanctions.” Barbara Geddes (1990) charges that many analyses of industrial policy have focused exclusively on the most successful cases—primarily the East Asian NICs—leading to biased inferences. Anna Breman and Carolyn Shelton (2001) show that case‐study work on the question of structural adjustment is systematically biased insofar as researchers tend to focus on disaster cases—those where structural adjustment is associated with very poor health and human development outcomes. These cases, often located in sub‐Saharan Africa, are by no means representative of the entire population. Consequently, scholarship on the question of structural adjustment is highly skewed in a particular ideological direction (against neoliberalism) (see also Gerring, Thacker, and Moreno 2005) .

These examples might be multiplied many times. Indeed, for many topics the most‐studied cases are acknowledged to be less than representative. It is worth reflecting upon the fact that our knowledge of the world is heavily colored by a few “big” (populous, rich, powerful) countries, and that a good portion of the disciplines of economics, political science, and sociology are built upon scholars' familiarity with the economics, political science, and sociology of one country, the United States. 23 Case‐study work is particularly prone to problems of investigator bias since so much rides on the researcher's selection of one (or a few) cases. Even if the investigator is unbiased, her sample may still be biased simply by virtue of “random” error (which may be understood as measurement error, error in the data‐generation process, or as an underlying causal feature of the universe).

There are only two situations in which a case‐study researcher need not be concerned with the representativeness of her chosen case. The first is the influential case research design, where a case is chosen because of its possible influence on a cross‐case model, and hence is not expected to be representative of a larger sample. The second is the deviant‐case method, where the chosen case is employed to confirm a broader cross‐case argument to which the case stands as an apparent exception. Yet even here the chosen case is expected to be representative of a broader set of cases—those, in particular, that are poorly explained by the extant model.

In all other circumstances, cases must be representative of the population of interest in whatever ways might be relevant to the proposition in question. Note that where a researcher is attempting to disconfirm a deterministic proposition the question of representativeness is perhaps more appropriately understood as a question of classification: Is the chosen case appropriately classified as a member of the designated population? If so, then it is fodder for a disconfirming case study.

If the researcher is attempting to confirm a deterministic proposition, or to make probabilistic arguments about a causal relationship, then the problem of representativeness is of the more usual sort: Is case A unit‐homogeneous relative to other cases in the population? This is not an easy matter to test. However, in a large‐ N context the residual for that case (in whatever model the researcher has greatest confidence in) is a reasonable place to start. Of course, this test is only as good as the model at hand. Any incorrect specifications or incorrect modeling procedures will likely bias the results and give an incorrect assessment of each case's “typicality.” In addition, there is the possibility of stochastic error, errors that cannot be modeled in a general framework. Given the explanatory weight that individual cases are asked to bear in a case‐study analysis, it is wise to consider more than just the residual test of representativeness. Deductive logic and an in‐depth knowledge of the case in question are often more reliable tools than the results of a cross‐case model.

In any case, there is no dispensing with the question. Case studies (with the two exceptions already noted) rest upon an assumed synecdoche: The case should stand for a population. If this is not true, or if there is reason to doubt this assumption, then the utility of the case study is brought severely into question.

Fortunately, there is some safety in numbers. Insofar as case‐study evidence is combined with cross‐case evidence the issue of sample bias is mitigated. Indeed, the suspicion of case‐study work that one finds in the social sciences today is, in my view, a product of a too‐literal interpretation of the case‐study method. A case study tout court is thought to mean a case study tout seul . Insofar as case studies and cross‐case studies can be enlisted within the same investigation (either in the same study or by reference to other studies in the same subfield), problems of representativeness are less worrisome. This is the virtue of cross‐level work, a.k.a. “triangulation.”

11 Ambiguities

Before concluding, I wish to draw attention to two ambiguities in case‐selection strategies in case‐study research. The first concerns the admixture of several case‐ selection strategies. The second concerns the changing status of a case as a study proceeds.

Some case studies follow only one strategy of case selection. They are typical , diverse , extreme , deviant , influential , crucial , pathway , most‐similar , or most‐different research designs, as discussed. However, many case studies mix and match among these case‐selection strategies. Indeed, insofar as all case studies seek representative samples, they are always in search of “typical” cases. Thus, it is common for writers to declare that their case is, for example, both extreme and typical; it has an extreme value on X   1 or Y but is not, in other respects, idiosyncratic. There is not much that one can say about these combinations of strategies except that, where the cases allow for a variety of empirical strategies, there is no reason not to pursue them. And where the same cases can serve several functions at once (without further effort on the researcher's part), there is little cost to a multi‐pronged approach to case analysis.

The second issue that deserves emphasis is the changing status of a case during the course of a researcher's investigation—which may last for years, if not decades. The problem is acute wherever a researcher begins in an exploratory mode and proceeds to hypothesis‐testing (that is, she develops a specific X   1 / Y proposition) or where the operative hypothesis or key control variable changes (a new causal factor is discovered or another outcome becomes the focus of analysis). Things change. And it is the mark of a good researcher to keep her mind open to new evidence and new insights. Too often, methodological discussions give the misleading impression that hypotheses are clear and remain fixed over the course of a study's development. Nothing could be further from the truth. The unofficial transcripts of academia— accessible in informal settings, where researchers let their guards down (particularly if inebriated)—are filled with stories about dead‐ends, unexpected findings, and drastically revised theory chapters. It would be interesting, in this vein, to compare published work with dissertation prospectuses and fellowship applications. I doubt if the correlation between these two stages of research is particularly strong.

Research, after all, is about discovery, not simply the verification or falsification of static hypotheses. That said, it is also true that research on a particular topic should move from hypothesis generating to hypothesis‐testing. This marks the progress of a field, and of a scholar's own work. As a rule, research that begins with an open‐ended ( X ‐ or Y ‐centered) analysis should conclude with a determinate X   1 / Y hypothesis.

The problem is that research strategies that are ideal for exploration are not always ideal for confirmation. The extreme‐case method is inherently exploratory since there is no clear causal hypothesis; the researcher is concerned merely to explore variation on a single dimension ( X or Y ). Other methods can be employed in either an open‐ ended (exploratory) or a hypothesis‐testing (confirmatory/disconfirmatory) mode. The difficulty is that once the researcher has arrived at a determinate hypothesis the originally chosen research design may no longer appear to be so well designed.

This is unfortunate, but inevitable. One cannot construct the perfect research design until (a) one has a specific hypothesis and (b) one is reasonably certain about what one is going to find “out there” in the empirical world. This is particularly true of observational research designs, but it also applies to many experimental research designs: Usually, there is a “good” (informative) finding, and a finding that is less insightful. In short, the perfect case‐study research design is usually apparent only ex post facto .

There are three ways to handle this. One can explain, straightforwardly, that the initial research was undertaken in an exploratory fashion, and therefore not constructed to test the specific hypothesis that is—now—the primary argument. Alternatively, one can try to redesign the study after the new (or revised) hypothesis has been formulated. This may require additional field research or perhaps the integration of additional cases or variables that can be obtained through secondary sources or through consultation of experts. A final approach is to simply jettison, or de‐emphasize, the portion of research that no longer addresses the (revised) key hypothesis. A three‐case study may become a two‐case study, and so forth. Lost time and effort are the costs of this downsizing.

In the event, practical considerations will probably determine which of these three strategies, or combinations of strategies, is to be followed. (They are not mutually exclusive.) The point to remember is that revision of one's cross‐case research design is normal and perhaps to be expected. Not all twists and turns on the meandering trail of truth can be anticipated.

12 Are There Other Methods of Case Selection?

At the outset of this chapter I summarized the task of case selection as a matter of achieving two objectives: representativeness (typicality) and variation (causal leverage). Evidently, there are other objectives as well. For example, one wishes to identify cases that are independent of each other. If chosen cases are affected by each other (sometimes known as Galton's problem or a problem of diffusion), this problem must be corrected before analysis can take place. I have neglected this issue because it is usually apparent to the researcher and, in any case, there are no simple techniques that might be utilized to correct for such biases. (For further discussion of this and other factors impinging upon case selection see Gerring 2001 , 178–81.)

I have also disregarded pragmatic/logistical issues that might affect case selection. Evidently, case selection is often influenced by a researcher's familiarity with the language of a country, a personal entrée into that locale, special access to important data, or funding that covers one archive rather than another. Pragmatic considerations are often—and quite rightly—decisive in the case‐selection process.

A final consideration concerns the theoretical prominence of a particular case within the literature on a subject. Researchers are sometimes obliged to study cases that have received extensive attention in previous studies. These are sometimes referred to as “paradigmatic” cases or “exemplars” ( Flyvbjerg 2004 , 427).

However, neither pragmatic/logistical utility nor theoretical prominence qualifies as a methodological factor in case selection. That is, these features of a case have no bearing on the validity of the findings stemming from a study. As such, it is appropriate to grant these issues a peripheral status in this chapter.

One final caveat must be issued. While it is traditional to distinguish among the tasks of case selection and case analysis, a close look at these processes shows them to be indistinct and overlapping. One cannot choose a case without considering the sort of analysis that it might be subjected to, and vice versa. Thus, the reader should consider choosing cases by employing the nine techniques laid out in this chapter along with any considerations that might be introduced by virtue of a case's quasi‐experimental qualities, a topic taken up elsewhere ( Gerring 2007 , ch. 6 ).

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Gujarati (2003) ; Kennedy (2003) . Interestingly, the potential of cross‐case statistics in helping to choose cases for in‐depth analysis is recognized in some of the earliest discussions of the case‐study method (e.g. Queen 1928 , 226).

This expands on Mill (1843/1872 , 253), who wrote of scientific enquiry as twofold: “either inquiries into the cause of a given effect or into the effects or properties of a given cause.”

This method has not received much attention on the part of qualitative methodologists; hence, the absence of a generally recognized name. It bears some resemblance to J. S. Mill's Joint Method of Agreement and Difference ( Mill 1843/1872 ), which is to say a mixture of most‐similar and most‐different analysis, as discussed below. Patton (2002 , 234) employs the concept of “maximum variation (heterogeneity) sampling.”

More precisely, George and Smoke (1974 , 534, 522–36, ch. 18 ; see also discussion in Collier and Mahoney 1996 , 78) set out to investigate causal pathways and discovered, through the course of their investigation of many cases, these three causal types. Yet, for our purposes what is important is that the final sample includes at least one representative of each “type.”

For further examples see Collier and Mahoney (1996) ; Geddes (1990) ; Tendler (1997) .

Traditionally, methodologists have conceptualized cases as having “positive” or “negative” values (e.g. Emigh 1997 ; Mahoney and Goertz 2004 ; Ragin 2000 , 60; 2004 , 126).

Geddes (1990) ; King, Keohane, and Verba (1994) . See also discussion in Brady and Collier (2004) ; Collier and Mahoney (1996) ; Rogowski (1995) .

The exception would be a circumstance in which the researcher intends to disprove a deterministic argument ( Dion 1998 ).

Geddes (2003 , 131). For other examples of casework from the annals of medicine see “Clinical reports” in the Lancet , “Case studies” in Canadian Medical Association Journal , and various issues of the Journal of Obstetrics and Gynecology , often devoted to clinical cases (discussed in Jenicek 2001 , 7). For examples from the subfield of comparative politics see Kazancigil (1994) .

For a discussion of the important role of anomalies in the development of scientific theorizing see Elman (2003) ; Lakatos (1978) . For examples of deviant‐case research designs in the social sciences see Amenta (1991) ; Coppedge (2004) ; Eckstein (1975) ; Emigh (1997) ; Kendall and Wolf (1949/1955) .

For examples of the crucial‐case method see Bennett, Lepgold, and Unger (1994) ; Desch (2002) ; Goodin and Smitsman (2000) ; Kemp (1986) ; Reilly and Phillpot (2003) . For general discussion see George and Bennett (2005) ; Levy (2002) ; Stinchcombe (1968 , 24–8).

A third position, which purports to be neither Popperian or Bayesian, has been articulated by Mayo (1996 , ch. 6 ). From this perspective, the same idea is articulated as a matter of “severe tests.”

It should be noted that Tsai's conclusions do not rest solely on this crucial case. Indeed, she employs a broad range of methodological tools, encompassing case‐study and cross‐case methods.

See also the discussion in Eckstein (1975) and Lijphart (1969) . For additional examples of case studies disconfirming general propositions of a deterministic nature see Allen (1965); Lipset, Trow, and Coleman (1956) ; Njolstad (1990) ; Reilly (2000–1) ; and discussion in Dion (1998) ; Rogowski (1995) .

Granted, insofar as case‐study analysis provides a window into causal mechanisms, and causal mechanisms are integral to a given theory, a single case may be enlisted to confirm or disconfirm a proposition. However, if the case study upholds a posited pattern of X/Y covariation, and finds fault only with the stipulated causal mechanism, it would be more accurate to say that the study forces the reformulation of a given theory, rather than its confirmation or disconfirmation. See further discussion in the following section.

Sometimes, the most‐similar method is known as the “method of difference,” after its inventor ( Mill 1843/1872 ). For later treatments see Cohen and Nagel (1934) ; Eggan (1954) ; Gerring (2001 , ch. 9 ); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) .

For good introductions see Ho et al. (2004) ; Morgan and Harding (2005) ; Rosenbaum (2004) ; Rosenbaum and Silber (2001) . For a discussion of matching procedures in Stata see Abadie et al. (2001) .

The most‐different method is also sometimes referred to as the “method of agreement,” following its inventor, J. S. Mill (1843/1872) . See also De Felice (1986) ; Gerring (2001 , 212–14); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) . For examples of this method see Collier and Collier (1991/2002) ; Converse and Dupeux (1962) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). However, most of these studies are described as combining most‐similar and most‐different methods.

In the following discussion I treat the terms social capital, civil society, and civic engagement interchangeably.

E.g. Collier and Collier (1991/2002) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). Karl (1997) , which affects to be a most‐different system analysis (20), is a particularly clear example of this. Her study, focused ostensibly on petro‐states (states with large oil reserves), makes two sorts of inferences. The first concerns the (usually) obstructive role of oil in political and economic development. The second sort of inference concerns variation within the population of petro‐states, showing that some countries (e.g. Norway, Indonesia) manage to avoid the pathologies brought on elsewhere by oil resources. When attempting to explain the constraining role of oil on petro‐states, Karl usually relies on contrasts between petro‐states and nonpetro‐states (e.g. ch. 10 ). Only when attempting to explain differences among petro‐states does she restrict her sample to petro‐states. In my opinion, very little use is made of the most‐different research design.

This was recognized, at least implicitly, by Mill (1843/1872 , 258–9). Skepticism has been echoed by methodologists in the intervening years (e.g. Cohen and Nagel 1934 , 251–6; Gerring 2001 ; Skocpol and Somers 1980 ). Indeed, explicit defenses of the most‐different method are rare (but see De Felice 1986 ).

Another way of stating this is to say that X is a “nontrivial necessary condition” of Y .

Wahlke (1979 , 13) writes of the failings of the “behavioralist” mode of political science analysis: “It rarely aims at generalization; research efforts have been confined essentially to case studies of single political systems, most of them dealing …with the American system.”

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Understanding Q-Methodology: Bridging the Gap Between Qualitative and Quantitative Research

By  Stella Smith, Ph.D.

Introduction

Among the myriad of methodologies, Q-methodology stands out as a unique approach that offers a nuanced understanding of subjectivity while maintaining the rigor of quantitative analysis (Damio, 2016; Herrington & Coogan, 2011). On April 2nd, the Research Methodology Group hosted a webinar focused on Q-methodology Essentials. In this blog post, we delve into the essence of Q-methodology, exploring its principles, applications, and significance in contemporary research. We will end with some suggestions for how to learn more about Q-methodology.

Q-methodology

Seeks to uncover subjective viewpoints or perspectives on a particular topic by systematically analyzing individuals' rankings of statements or items

What is Q-Methodology?

Q-methodology, developed by British physicist and psychologist William Stephenson, is a research technique that combines elements of both qualitative and quantitative methodologies (Stephenson,1953). At its core, Q-methodology seeks to uncover subjective viewpoints or perspectives on a particular topic by systematically analyzing individuals' rankings of statements or items (Sandling, 2022; Van Exel & De Graaf, 2005). Unlike traditional surveys or interviews, which aim to capture consensus or frequency of responses, Q-methodology focuses on understanding the diversity of opinions within a given population.

Principles of Q-Methodology

Central to Q-methodology is the notion of "subjectivity" – recognizing that individuals interpret the world differently based on their unique experiences, beliefs, and values. The process typically involves three main steps:

Statement Generation: Researchers compile a set of statements or items relevant to the topic under study. These statements should cover a wide range of viewpoints and perspectives to capture the diversity within the population.

Q-Sorting: Participants are presented with the statements and asked to rank them according to their level of agreement or preference. This process, known as Q-sorting, requires participants to make subjective judgments about the statements based on their personal viewpoints.

Factor Analysis: The Q-sort data from multiple participants are then subjected to factor analysis, a statistical technique that identifies patterns or "factors" representing clusters of similar viewpoints. Through factor analysis, researchers can uncover underlying dimensions of opinion within the dataset.

Applications of Q-Methodology

Q-methodology has found applications across various disciplines, including psychology, sociology, political science, and market research. Some common areas of application include exploring subjective perceptions, understanding stakeholder perspectives and market segmentation.

Significance of Q-Methodology

What distinguishes Q-methodology is its ability to reconcile the richness of qualitative data with the rigor of quantitative analysis. By acknowledging the subjective nature of human perception while employing robust statistical techniques, Q-methodology offers a holistic approach to understanding complex social phenomena (Herrington & Coogan, 2011).

Moreover, Q-methodology provides a platform for amplifying marginalized voices and uncovering minority viewpoints that may be overlooked in traditional research approaches. By embracing diversity and embracing subjectivity, Q-methodology fosters a more inclusive and comprehensive understanding of the world around us.

Want to know more?

Check out the full webinar on Q-methodology which is uploaded to the  Research and Methodology Group Teams  site. 

Schedule an  office hours appointment  with a methodologist to discuss your Q-methodology design.

Review the  Qmethod  website and  Operant Subjectivity - The International Journal of Q Methodology

Damio, S. M. (2016). Q Methodology: An Overview and Steps to Implementation. Asian Journal of  University Education, 12(1), 105.

Herrington, N., &, Coogan, J. (2011). Q methodology: an overview. Research in Teacher   Education, 1(2), 24-28.

Sandling, J. (2022). Q Methodology: Complete Beginner’s Guide. Available at   https://jonathansandling.com/q-methodology-complete-beginners-guide/

Stephenson W. The study of behavior: Q-technique and its methodology. Chicago: University of Chicago Press. 1953

Van Exel, J., & De Graaf, G. (2005). Q methodology: A sneak preview. Available at https://www.betterevaluation.org/tools-resources/q-methodology-sneak-preview

Stella Smith, Ph.D.

ABOUT THE AUTHOR

Dr. Stella Smith serves as the Associate University Research Chair for Center for Educational and Instructional Technology Research (CEITR).  She is also an Assistant Professor of Qualitative Research at Prairie View A&M University. A qualitative researcher, Dr. Stella Smith's scholarly interests focus on the experiences of  African American females in leadership in higher education; diversity, equity and inclusion of underserved populations in higher education, and P–20 educational pipeline alignment.  Dr. Smith is a strong advocate for social justice and passionate about creating asset based pathways of success for underserved students.

Dr. Smith was recognized with a 2014 Dissertation Award from the American Association of Blacks in Higher Education and as part of the 2019 class of 35 Outstanding Women Leaders in Higher Education by Diverse Issues in Higher Education. Dr. Smith earned her PhD in Educational Administration with a portfolio in Women and Gender Studies from The University of Texas at Austin.

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• Published: 26 April 2024

Systematic review on the frequency and quality of reporting patient and public involvement in patient safety research

• Sahar Hammoud   ORCID: orcid.org/0000-0003-4682-9001 1 ,
• Laith Alsabek 1 , 2 ,
• Lisa Rogers 1 &
• Eilish McAuliffe 1  

BMC Health Services Research volume  24 , Article number:  532 ( 2024 ) Cite this article

Metrics details

In recent years, patient and public involvement (PPI) in research has significantly increased; however, the reporting of PPI remains poor. The Guidance for Reporting Involvement of Patients and the Public (GRIPP2) was developed to enhance the quality and consistency of PPI reporting. The objective of this systematic review is to identify the frequency and quality of PPI reporting in patient safety (PS) research using the GRIPP2 checklist.

Searches were performed in Ovid MEDLINE, EMBASE, PsycINFO, and CINAHL from 2018 to December, 2023. Studies on PPI in PS research were included. We included empirical qualitative, quantitative, mixed methods, and case studies. Only articles published in peer-reviewed journals in English were included. The quality of PPI reporting was assessed using the short form of the (GRIPP2-SF) checklist.

A total of 8561 studies were retrieved from database searches, updates, and reference checks, of which 82 met the eligibility criteria and were included in this review. Major PS topics were related to medication safety, general PS, and fall prevention. Patient representatives, advocates, patient advisory groups, patients, service users, and health consumers were the most involved. The main involvement across the studies was in commenting on or developing research materials. Only 6.1% ( n  = 5) of the studies reported PPI as per the GRIPP2 checklist. Regarding the quality of reporting following the GRIPP2-SF criteria, our findings show sub-optimal reporting mainly due to failures in: critically reflecting on PPI in the study; reporting the aim of PPI in the study; and reporting the extent to which PPI influenced the study overall.

Conclusions

Our review shows a low frequency of PPI reporting in PS research using the GRIPP2 checklist. Furthermore, it reveals a sub-optimal quality in PPI reporting following GRIPP2-SF items. Researchers, funders, publishers, and journals need to promote consistent and transparent PPI reporting following internationally developed reporting guidelines such as the GRIPP2. Evidence-based guidelines for reporting PPI should be encouraged and supported as it helps future researchers to plan and report PPI more effectively.

Trial registration

The review protocol is registered with PROSPERO (CRD42023450715).

Peer Review reports

Patient safety (PS) is defined as “the absence of preventable harm to a patient and reduction of risk of unnecessary harm associated with healthcare to an acceptable minimum” [ 1 ]. It is estimated that one in 10 patients are harmed in healthcare settings due to unsafe care, resulting in over three million deaths annually [ 2 ]. More than 50% of adverse events are preventable, and half of these events are related to medications [ 3 , 4 ]. There are various types of adverse events that patients can experience such as medication errors, patient falls, healthcare-associated infections, diagnostic errors, pressure ulcers, unsafe surgical procedures, patient misidentification, and others [ 1 ].

Over the last few decades, the approach of PS management has shifted toward actively involving patients and their families in managing PS. This innovative approach has surpassed the traditional model where healthcare providers were the sole managers of PS [ 5 ]. Recent research has shown that patients have a vital role in promoting their safety and decreasing the occurrence of adverse events [ 6 ]. Hence, there is a growing recognition of patient and family involvement as a promising method to enhance PS [ 7 ]. This approach includes involving patients in PS policy development, research, and shared decision making [ 1 ].

In the last decade, research involving patients and the public has significantly increased. In the United Kingdom (U.K), the National Institute for Health Research (NIHR) has played a critical role in providing strategic and infrastructure support to integrate Public and Patient Involvement (PPI) throughout publicly funded research [ 8 ]. This has established a context where PPI is recognised as an essential element in research [ 9 ]. In Ireland, the national government agency responsible for the management and delivery of all public health and social services; the National Health Service Executive (HSE) emphasise the importance of PPI in research and provide guidance for researchers on how to involve patients and public in all parts of the research cycle and knowledge translation process [ 10 ]. Similar initiatives are also developing among other European countries, North America, and Australia. However, despite this significant expansion of PPI research, the reporting of PPI in research articles continues to be sub-optimal, inconsistent, and lacks essential information on the context, process, and impact of PPI [ 9 ]. To address this problem, the Guidance for Reporting Involvement of Patients and the Public (GRIPP) was developed in 2011 following the EQUATOR methodology to enhance the quality, consistency, and transparency of PPI reporting. Additionally, to provide guidance for researchers, patients, and the public to advance the quality of the international PPI evidence-base [ 11 ]. The first GRIPP checklist was a significant start in producing higher-quality PPI reporting; however, it was developed following a systematic review, and did not include any input from the international PPI research community. Given the importance of reaching consensus in generating current reporting guidelines, a second version of the GRIPP checklist (GRIPP2) was developed to tackle this problem by involving the international PPI community in its development [ 9 ]. There are two versions of the GRIPP2 checklist, a long form (GRIPP2-LF) for studies with PPI as the primary focus, and a short form (GRIPP2-SF) for studies with PPI as secondary or tertiary focus.

Since the publication of the GRIPP2 checklist, several systematic reviews have been conducted to assess the quality of PPI reporting on various topics. For instance, Bergin et al. in their review to investigate the nature and impact of PPI in cancer research, reported a sub-optimal quality of PPI reporting using the GRIPP2-SF, mainly due to failure to address PPI challenges [ 12 ]. Similarly, Owyang et al. in their systematic review to assess the prevalence, extent, and quality of PPI in orthopaedic practice, described a poor PPI reporting following the GRIPP2-SF checklist criteria [ 13 ]. While a few systematic reviews have been conducted to assess theories, strategies, types of interventions, and barriers and enablers of PPI in PS [ 5 , 14 , 15 , 16 ], no previous review has assessed the quality of PPI reporting in PS research. Thus, our systematic review aims to address this knowledge gap. The objective of this review is to identify the frequency PPI reporting in PS research using the GRIPP2 checklist from 2018 (the year after GRIPP2 was published) and the quality of reporting following the GRIPP2-SF. The GRIPP2 checklist was chosen as the benchmark as it is the first international, evidence-based, community consensus informed guideline for the reporting of PPI in research and more specifically in health and social care research [ 9 ]. Additionally, it is the most recent report-focused framework and the most recommended by several leading journals [ 17 ].

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to plan and report this review [ 18 ]. The review protocol was published on PROSPERO the International Database of Prospectively Registered Systematic Reviews in August 2023 (CRD42023450715).

Search strategy

For this review, we used the PICo framework to define the key elements in our research. These included articles on patients and public (P-Population) involvement (I- phenomenon of Interest) in PS (C-context). Details are presented in Table  1 . Four databases were searched including Ovid MEDLINE, EMBASE, PsycINFO, and CINAHL to identify papers on PPI in PS research. A systematic search strategy was initially developed using MEDLINE. MeSH terms and keywords relevant to specific categories (e.g., patient safety) were combined using the “OR” Boolean term (i.e. patient safety OR adverse event OR medical error OR surgical error) and categories were then combined using the “AND” Boolean term. (i.e. “patient and public involvement” AND “patient safety”). The search strategy was adapted for the other three databases. Full search strategies are provided in Supplementary file 1 . The search was conducted on July 27th, 2023, and was limited to papers published from 2018. As the GRIPP2 tool was published in 2017, this limit ensured the retrieval of relevant studies. An alert system was set on the four databases to receive all new published studies until December 2023, prior to the final analysis. The search was conducted without restrictions on study type, research design, and language. To reduce selection bias, hand searching was carried out on the reference lists of all the eligible articles in the later stages of the review. This was done by the first author. The search strategy was developed by the first author and confirmed by the research team and a Librarian. The database search was conducted by the first author.

Inclusion and exclusion criteria

Studies on PPI in PS research with a focus on health/healthcare were included in this review. We defined PPI as active involvement which is in line with the NIHR INVOLVE definition as “research being carried out ‘with’ or ‘by’ members of the public rather than ‘to’, ‘about’ or ‘for’ them” [ 19 ]. This includes any PPI including, being a co-applicant on a research project or grant application, identifying research priorities, being a member of an advisory or steering group, participating in developing research materials or giving feedback on them, conducting interviews with study participants, participating in recruitment, data collection, data analysis, drafting manuscripts and/or dissemination of results. Accordingly, we excluded studies where patients or the public were only involved as research participants.

We defined patients and public to include patients, relatives, carers, caregivers and community, which is also in line with the NIHR PPI involvement in National Health Service [ 19 ].

Patient safety included topics on medication safety, adverse events, communication, safety culture, diagnostic errors, and others. A full list of the used terms for PPI and PS is provided in Supplementary file 1 . Regarding the research type and design, we included empirical qualitative, quantitative, mixed methods, and case studies. Only articles published in peer-reviewed journals and in English were included.

Any article that did not meet the inclusion criteria was excluded. Studies not reporting outcomes were excluded. Furthermore, review papers, conference abstracts, letters to editor, commentary, viewpoints, and short communications were excluded. Finally, papers published prior to 2018 were excluded.

Study selection

The selection of eligible studies was done by the first and the second authors independently, starting with title and abstracts screening to eliminate papers that failed to meet our inclusion criteria. Then, full text screening was conducted to decide on the final included papers in this review. Covidence, an online data management system supported the review process, ensuring reviewers were blinded to each other’s decisions. Disagreements between reviewers were discussed first, in cases where the disagreement was not resolved, the fourth author was consulted.

Data extraction and analysis

A data extraction sheet was developed using excel then piloted, discussed with the research team and modified as appropriate. The following data were extracted: citation and year of publication, objective of the study, country, PS topic, design, setting, PPI participants, PPI stages (identifying research priorities, being a member of an advisory or steering group, etc.…), frequency of PPI reporting as per the GRIPP2 checklist, and the availability of a plain language summary. Additionally, data against the five items of GRIPP2-SF (aim of PPI in the study, methods used for PPI, outcomes of PPI including the results and the extent to which PPI influenced the study overall, and reflections on PPI) were extracted. To avoid multiple publication bias and missing outcomes, data extraction was done by the first and the second authors independently and then compared. Disagreements between reviewers were first discussed, and then resolved by the third and fourth authors if needed.

Quality assessment

The quality of PPI reporting was assessed using GRIPP2-SF developed by Staniszewska et al. [ 9 ] as it is developed to improve the quality, consistency, and reporting of PPI in social and healthcare research. Additionally the GRIPP2-SF is suitable for all studies regardless of whether PPI is the primary, secondary, or tertiary focus, whereas the GRIPP2-LF is not suitable for studies where PPI serves as a secondary or tertiary focus. The checklist includes five items (mentioned above) that authors should include in their studies. It is important to mention that Staniszewska et al. noted that “while GRIPP2-SF aims to guide consistent reporting, it is not possible to be prescriptive about the exact content of each item, as the current evidence-base is not advanced enough to make this possible” ([ 9 ] p5). For that reason, we had to develop criteria for scoring the five reporting items. We used three scoring as Yes, No, and partial for each of the five items of the GRIPP2-SF. Yes, was given when authors presented PPI information on the item clearly in the paper. No, when no information was provided, and partial when the information partially met the item requirement. For example, as per GRIPP2-SF authors should provide a clear description of the methods used for PPI in the study. In the example given by Staniszewska et al., information on patient/public partners and how many of them were provided, as well as the stages of the study they were involved in (i.e. refining the focus of the research questions, developing the search strategy, interpreting results). Thus, in our evaluation of the included studies, we gave a yes if information on PPI participants (i.e. patient partners, community partners, or family members etc..) and how many of them were involved was provided, and information on the stages or actions of their involvement in the study was provided. However, we gave a “partial” if information was not fully provided (i.e. information on patient/public partners and how many were involved in the study without describing in what stages or actions they were involved, and vice versa), and a “No” if no information was presented at all.

The quality of PPI reporting was done by the first and the second authors independently and then compared. Disagreements between reviewers were first discussed, and then resolved by the third and fourth author when needed.

Assessing the quality or risk of bias of the included studies was omitted, as the focus in this review was on appraising the quality of PPI reporting rather than assessing the quality of each research article.

Data synthesis

After data extraction, a table summarising the included studies was developed. Studies were compared according to the main outcomes of the review; frequency of PPI reporting following the GRIPP2 checklist and the quality of reporting as per GRIPP2-SF five items, and the availability of a plain language summary.

Search results and study selection

The database searches yielded a total of 8491 studies. First, 2496 were removed as duplicates. Then, after title and abstract screening, 5785 articles were excluded leaving 210 articles eligible for the full text review. After a careful examination, 68 of these studies were included in this review. A further 38 studies were identified from the alert system that was set on the four databases and 32 studies from the reference check of the included studies. Of these 70 articles, 56 were further excluded and 14 were added to the previous 68 included studies. Thus, 82 studies met the inclusion criteria and were included in this review. A summary of the database search results and the study selection process are presented in Fig.  1 .

PRISMA flow diagram of the study selection process. The PRISMA flow diagram details the review search results and selection process

Overview of included studies

Details of the study characteristics including first author and year of publication, objective, country, study design, setting, PS topic, PPI participants and involvement stages are presented in Supplementary file 2 . The majority of the studies were conducted in the U.K ( n  = 24) and the United States of America ( n  = 18), with the remaining 39 conducted in other high income countries, the exception being one study in Haiti. A range of study designs were identified, the most common being qualitative ( n  = 31), mixed methods ( n  = 13), interventional ( n  = 5), and quality improvement projects ( n  = 4). Most PS topics concerned medication safety ( n  = 17), PS in general (e.g., developing a PS survey or PS management application) ( n  = 14), fall prevention ( n  = 13), communication ( n  = 11), and adverse events ( n  = 10), with the remaining PS topics listed in Supplementary file 2 .

Patient representatives, advocates, and patient advisory groups ( n  = 33) and patients, service users, and health consumers ( n  = 32) were the main groups involved. The remaining, included community members/ organisations. Concerning PPI stages, the main involvement across the studies was in commenting on or developing research materials ( n  = 74) including, patient leaflets, interventional tools, mobile applications, and survey instruments. Following this stage, involvement in data analysis, drafting manuscripts, and disseminating results ( n  = 30), and being a member of a project advisory or steering group ( n  = 18) were the most common PPI evident in included studies. Whereas the least involvement was in identifying research priorities ( n  = 5), and being a co-applicant on a research project or grant application ( n  = 6).

Regarding plain language summary, only one out of the 82 studies (1.22%) provided a plain language summary in their paper [ 20 ].

Frequency and quality of PPI reporting

The frequency of PPI reporting following the GRIPP2 checklist was 6.1%, where only five of the 82 included studies reported PPI in their papers following the GRIPP2 checklist. The quality of PPI reporting in those studies is presented in Table  2 . Of these five studies, one study (20%) did not report the aim of PPI in the study and one (20%) did not comment on the extent to which PPI influenced the study overall.

The quality of PPI reporting of the remaining 77 studies is presented in Table  3 . The aim of PPI in the study was reported in 62.3% of articles ( n  = 48), while 3.9% ( n  = 3) partially reported this. A clear description of the methods used for PPI in the study was reported in 79.2% of papers ( n  = 61) and partially in 20.8% ( n  = 16). Concerning the outcomes, 81.8% of papers ( n  = 63) reported the results of PPI in the study, while 10.4% ( n  = 8) partially did. Of the 77 studies, 68.8% ( n  = 53) reported the extent to which PPI influenced the study overall and 3.9% ( n  = 3) partially reported this. Finally, 57.1% ( n  = 44) of papers critically reflected on the things that went well and those that did not and 2.6% ( n  = 2) partially reflected on this.

Summary of main findings

This systematic review assessed the frequency of reporting PPI in PS research using the GRIPP2 checklist and quality of reporting using the GRIPP2-SF. In total, 82 studies were included in this review. Major PS topics were related to medication safety, general PS, and fall prevention. Patient representatives, advocates, patient advisory groups, patients, service users, and health consumers were the most involved. The main involvement across the studies was in commenting on or developing research materials such as educational and interventional tools, survey instruments, and applications while the least was in identifying research priorities and being a co-applicant on a research project or grant application. Thus, significant effort is still needed to involve patients and the public in the earlier stages of the research process given the fundamental impact of PS on their lives.

Overall completeness and applicability of evidence

A low frequency of reporting PPI in PS research following the GRIPP2 guidelines was revealed in this review, where only five of the 82 studies included mentioned that PPI was reported as per the GRIPP2 checklist. This is despite it being the most recent report-focused framework and the most recommended by several leading journals [ 17 ]. This was not surprising as similar results were reported in recent reviews in other healthcare topics. For instance, Musbahi et al. in their systematic review on PPI reporting in bariatric research reported that none of the 90 papers identified in their review mentioned or utilised the GRIPP2 checklist [ 102 ]. Similarly, a study on PPI in orthodontic research found that none of the 363 included articles reported PPI against the GRIPP2 checklist [ 103 ].

In relation to the quality of reporting following the GRIPP2-SF criteria, our findings show sub-optimal reporting within the 77 studies that did not use GRIPP2 as a guide/checklist to report their PPI. Similarly, Bergin et al. in their systematic review to investigate the nature and impact of PPI in cancer research concluded that substandard reporting was evident [ 12 ]. In our review, this was mainly due to failure to meet three criteria. First, the lowest percentage of reporting (57.1%, n  = 44) was related to critical reflection on PPI in the study (i.e., what went well and what did not). In total, 31 studies (42.9%) did not provide any information on this, and two studies were scored as partial. The first study mentioned that only involving one patient was a limitation [ 27 ] and the other stated that including three patients in the design of the tool was a strength [ 83 ]. Both studies did not critically comment or reflect on these points so that future researchers are able to avoid such problems and enhance PPI opportunities. For instance, providing the reasons/challenges behind the exclusive inclusion of a single patient and explaining how this limits the study findings and conclusion would help future researchers to address these challenges. Likewise, commenting on why incorporating three patients in the design of the study tool could be seen as a strength would have been beneficial. This could be, fostering diverse perspectives and generating novel ideas for developing the tool. Similar to our findings, Bergin et al. in their systematic review reported that 40% of the studies failed to meet this criterion [ 12 ].

Second, only 48 out of 77 articles (62.3%) reported the aim of PPI in their study, which is unlike the results of Bergin et al. where most of the studies (93.1%) in their review met this criterion [ 12 ]. Of the 29 studies which did not meet this criterion in our review, few mentioned in their objective developing a consensus-based instrument [ 41 ], reaching a consensus on the patient-reported outcomes [ 32 ], obtaining international consensus on a set of core outcome measures [ 98 ], and facilitating a multi-stakeholder dialogue [ 71 ] yet, without indicating anything in relation to patients, patient representatives, community members, or any other PPI participants. Thus, the lack of reporting the aim of PPI was clearly evident in this review. Reporting the aim of PPI in the study is crucial for promoting transparency, methodological rigor, reproducibility, and impact assessment of the PPI.

Third, 68.8% ( n  = 53) of the studies reported the extent to which PPI influenced the study overall including positive and negative effects if any. This was again similar to the findings of Bergin et al., where 38% of the studies did not meet this criterion mainly due to a failure to address PPI challenges in their respective studies [ 12 ]. Additionally, Owyang et al. in their review on the extent, and quality of PPI in orthopaedic practice, also described a poor reporting of PPI impact on research [ 13 ]. As per the GRIPP2 guidelines, both positive and negative effects of PPI on the study should be reported when applicable. Providing such information is essential as it enhances future research on PPI in terms of both practice and reporting.

Reporting a clear description of the methods used for PPI in the study was acceptable, with 79.2% of the papers meeting this criterion. Most studies provided information in the methods section of their papers on the PPI participants, their number, stages of their involvement and how they were involved. Providing clear information on the methods used for PPI is vital to give the reader a clear understanding of the steps taken to involve patients, and for other researchers to replicate these methods in future research. Additionally, reporting the results of PPI in the study was also acceptable with 81.8% of the papers reporting the outcomes of PPI in the results section. Reporting the results of PPI is important for enhancing methodological transparency, providing a more accurate interpretation for the study findings, contributing to the overall accountability and credibility of the research, and informing decision making.

Out of the 82 studies included in this review, only one study provided a plain language summary. We understand that PS research or health and medical research in general is difficult for patients and the public to understand given their diverse health literacy and educational backgrounds. However, if we expect patients and the public to be involved in research then, it is crucial to translate this research that has a huge impact on their lives into an easily accessible format. Failing to translate the benefits that such research may have on patient and public lives may result in them underestimating the value of this research and losing interest in being involved in the planning or implementation of future research [ 103 ]. Thus, providing a plain language summary for research is one way to tackle this problem. To our knowledge, only a few health and social care journals (i.e. Cochrane and BMC Research Involvement and Engagement) necessitate a plain language summary as a submission requirement. Having this as a requirement for submission is crucial in bringing the importance of this issue to researchers’ attention.

Research from recent years suggests that poor PPI reporting in articles relates to a lack of submission requirements for PPI reporting in journals and difficulties with word limits for submitted manuscripts [ 13 ]. Price et al. assessed the frequency of PPI reporting in published papers before and after the introduction of PPI reporting obligations by the British Medical Journal (BMJ) [ 104 ]. The authors identified an increase in PPI reporting in papers published by BMJ from 0.5% to 11% between the periods of 2013–2014 and 2015–2016. The study findings demonstrate the impact of journal guidelines in shaping higher quality research outputs [ 13 ]. In our review, we found a low frequency of PPI reporting in PS research using the GRIPP2 checklist, alongside sub-optimal quality of reporting following GRIPP2-SF. This could potentially be attributed to the absence of submission requirements for PPI reporting in journals following the GRIPP2 checklist, as well as challenges posed by word limits.

Strengths and limitations

This systematic review presents an overview on the frequency of PPI reporting in PS research using the GRIPP2 checklist, as well as an evaluation of the quality of reporting following the GRIPP2-SF. As the first review to focus on PS research, it provides useful knowledge on the status of PPI reporting in this field, and the extent to which researchers are adopting and adhering to PPI reporting guidelines. Despite these strengths, our review has some limitations that should be mentioned. First, only English language papers were included in this review due to being the main language of the researchers. Thus, there is a possibility that relevant articles on PPI in PS research may have been omitted. Another limitation is related to our search which was limited to papers published starting 2018 as the GRIPP2 guidelines were published in 2017. Thus it is probable that the protocols of some of these studies were developed earlier than the publication of the GRIPP2 checklist, meaning that PPI reporting following GRIPP2 was not common practice and thus not adopted by these studies. This might limit the conclusions we can draw from this review. Finally, the use of GRIPP2 to assess the quality of PPI reporting might be a limitation as usability testing has not yet been conducted to understand how the checklist works in practice with various types of research designs. However, the GRIPP2 is the first international, evidence-based, community consensus informed guideline for the reporting of PPI in health and social care research. Reflections and comments from researchers using the GRIPP2 will help improve its use in future studies.

Implications for research and practice

Lack of PPI reporting not only affects the quality of research but also implies that others cannot learn from previous research experience. Additionally, without consistent and transparent reporting it is difficult to evaluate the impact of various PPI in research [ 9 ]: “if it is not reported it cannot be assessed” ([ 105 ] p19). Enhanced PPI reporting will result in a wider range and richer high-quality evidence-based PPI research, leading to a better understanding of PPI use and effectiveness [ 103 ]. GRIPP2 reporting guidelines were developed to provide guidance for researchers, patients, and the public to enhance the quality of PPI reporting and improve the quality of the international PPI evidence-base. The guidance can be used prospectively to plan PPI or retrospectively to guide the structure or PPI reporting in research [ 9 ]. To enhance PPI reporting, we recommend the following;

Publishers and journals

First, we encourage publishers and journals to require researchers to report PPI following the GRIPP2 checklist. Utilising the short or the long version should depend on the primary focus of the study (i.e., if PPI is within the primary focus of the research then the GRIPP2-LF is recommended). Second, we recommend that journals and editorial members advise reviewers to evaluate PPI reporting within research articles following the GRIPP2 tool and make suggestions accordingly. Finally, we encourage journals to add a plain language summary as a submission requirement to increase research dissemination and improve the accessibility of research for patients and the public.

Researchers

Though there is greater evidence of PPI in research, it is still primarily the researchers that are setting the research agenda and deciding on the research questions to be addressed. Thus, significant effort is still needed to involve patients and the public in the earlier stages of the research process given the fundamental impact of PS on their lives. To enhance future PPI reporting, perhaps adding a criterion following the GRIPP2 tool to existing EQUATOR checklists for reporting research papers such as STROBE, PRISMA, CONSORT, may support higher quality research. Additionally, currently, there is no detailed explanation paper for the GRIPP2 where each criterion is explained in detail with examples. Addressing this gap would be of great benefit to guide the structure of PPI reporting and to explore the applicability of each criterion in relation to different stages of PPI in research. For instance, having a detailed explanation for each criterion across different research studies having various PPI stages would be of high value to improve future PPI reporting given the growing interest in PPI research in recent years and the relatively small PPI evidence base in health and medical research.

Funding bodies can also enhance PPI reporting by adding a requirement for researchers to report PPI following the GRIPP2 checklist. In Ireland, the National HSE has already initiated this by requiring all PPI in HSE research in Ireland to be reported following the GRIPP2 guidelines [ 10 ].

This study represents the first systematic review on the frequency and quality of PPI reporting in PS research using the GRIPP2 checklist. Most PS topics were related to medication safety, general PS, and fall prevention. The main involvement across the studies was in commenting on or developing research materials. Thus, efforts are still needed to involve patients and the public across all aspects of the research process, especially earlier stages of the research cycle. The frequency of PPI reporting following the GRIPP2 guidelines was low, and the quality of reporting following the GRIPP2-SF criteria was sub-optimal. The lowest percentages of reporting were on critically reflecting on PPI in the study so future research can learn from this experience and work to improve it, reporting the aim of the PPI in the study, and reporting the extent to which PPI influenced the study overall including positive and negative effects. Researchers, funders, publishers, journals, editorial members and reviewers have a responsibility to promote consistent and transparent PPI reporting following internationally developed reporting guidelines such as the GRIPP2. Evidence-based guidelines for reporting PPI should be supported to help future researchers plan and report PPI more effectively, which may ultimately improve the quality and relevance of research.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its Supplementary information files.

Abbreviations

• Patient safety

United Kingdom

National Institute for Health Research

Public and Patient Involvement

Health Service Executive

Guidance for Reporting Involvement of Patients and the Public

Second version of the GRIPP checklist

Long form of GRIPP2

Short form of GRIPP2

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

The International Database of Prospectively Registered Systematic Reviews

British Medical Journal

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This research is funded as part of the Collective Leadership and Safety Cultures (Co-Lead) research programme which is funded by the Irish Health Research Board, grant reference number RL-2015–1588 and the Health Service Executive. The funders had no role in the study conceptualisation, design, data collection, analysis, decision to publish, or preparation of the manuscript.

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S.H and E.M.A designed the study. S.H developed the search strategies with feedback from L.A, L.R, and E.M.A. S.H conducted all searches. S.H and L.A screened the studies, extracted the data, and assessed the quality of PPI reporting. S.H analysed the data with feedback from E.M.A. S.H drafted the manuscript. All authors revised and approved the submitted manuscript. All authors agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Hammoud, S., Alsabek, L., Rogers, L. et al. Systematic review on the frequency and quality of reporting patient and public involvement in patient safety research. BMC Health Serv Res 24 , 532 (2024). https://doi.org/10.1186/s12913-024-11021-z

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A mixed methods evaluation of the impact of ECHO ® telementoring model for capacity building of community health workers in India

• Rajmohan Panda 1 ,
• Supriya Lahoti   ORCID: orcid.org/0000-0001-6826-5273 2 ,
• Nivedita Mishra 2 ,
• Rajath R. Prabhu 3 ,
• Kalpana Singh 4 ,
• Apoorva Karan Rai 2 &
• Kumud Rai 2  

Human Resources for Health volume  22 , Article number:  26 ( 2024 ) Cite this article

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Introduction

India has the largest cohort of community health workers with one million Accredited Social Health Activists (ASHAs). ASHAs play vital role in providing health education and promoting accessible health care services in the community. Despite their potential to improve the health status of people, they remain largely underutilized because of their limited knowledge and skills. Considering this gap, Extension for Community Healthcare Outcomes (ECHO) ® India, in collaboration with the National Health System Resource Centre (NHSRC), implemented a 15-h (over 6 months) refresher training for ASHAs using a telementoring interface. The present study intends to assess the impact of the training program for improving the knowledge and skills of ASHA workers.

We conducted a pre–post quasi-experimental study using a convergent parallel mixed-method approach. The quantitative survey ( n  = 490) assessed learning competence, performance, and satisfaction of the ASHAs. In addition to the above, in-depth interviews with ASHAs ( n  = 12) and key informant interviews with other stakeholders ( n  = 9) examined the experience and practical applications of the training. Inferences from the quantitative and qualitative approaches were integrated during the reporting stage and presented using an adapted Moore’s Expanded Outcomes Framework.

There was a statistically significant improvement in learning ( p =  0.038) and competence ( p =  0.01) after attending the training. Participants were satisfied with the opportunity provided by the teleECHO™ sessions to upgrade their knowledge. However, internet connectivity, duration and number of participants in the sessions were identified as areas that needed improvement for future training programs. An improvement in confidence to communicate more effectively with the community was reported. Positive changes in the attitudes of ASHAs towards patient and community members were also reported after attending the training. The peer-to-peer learning through case-based discussion approach helped ensure that the training was relevant to the needs and work of the ASHAs.

Conclusions

The ECHO Model ™ was found effective in improving and updating the knowledge and skills of ASHAs across different geographies in India. Efforts directed towards knowledge upgradation of ASHAs are crucial for strengthening the health system at the community level. The findings of this study can be used to guide future training programs.

Trial registration The study has been registered at the Clinical Trials Registry, India (CTRI/2021/10/037189) dated 08/10/2021.

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The Alma Ata Declaration of 1978 has recognized primary health care as an essential element for improving community health. Community health workers (CHWs) have the potential to complement an overstrained health workforce and enhance primary healthcare access and quality [ 1 ]. Low- and middle-income countries (LMICs) face a triple burden of low density of doctors and nurse-midwives, low government expenditure on health, and disproportionately larger poor health outcomes [ 2 ]. The roles and responsibilities of CHWs vary across LMICs [ 3 ]. A systematic review has documented that the socio-cultural, economic, health system, and political context in which CHW interventions operate in LMICs influence the implementation and success of interventions [ 4 ].

The National Rural Health Mission (NRHM), India introduced Accredited Social Health Activists (ASHAs) as female CHWs in 2005. The ASHAs are women volunteers selected from the local village and were initially conceptualized with a vision to improve maternal and child health in the country; however, over time, they are now involved in different national health programmes [ 5 , 6 ]. Despite their potential to contribute to preventive and promotive healthcare, they remain largely underutilized because of their limited knowledge and skills [ 1 ]. The World Health Organisation (WHO) has suggested ‘regular training and supervision’ for CHWs to fulfil their role successfully [ 7 ]. In India, the health system lacks methods for continuous education and routine upgradation of the ASHA’s skills [ 8 , 9 , 10 ].

In LMICs, digital training programs can help expand the reach of training to large numbers of healthcare workers at a low cost without interfering with the delivery of routine healthcare services [ 11 , 12 ]. An evidence-mapping study of 88 studies that used technology for training CHWs in LMICs found that the focus of trainings was maternal and child health and other high-burden diseases were neglected [ 13 ]. In India, studies evaluating digital trainings for CHWs have focussed on specific diseases or have been limited to specific states in the last decade [ 10 , 14 ]. This study was conducted across multiple states. More such studies with larger sample size are needed on the evaluation of such training initiatives in India [ 13 , 15 , 16 ].

Project Extension for Community Healthcare Outcomes (ECHO) presents an educational opportunity for capacity-building through a telementoring platform that uses video conferencing to create a continuous loop of learning and peer support. The sessions are facilitated by didactic presentation and case-based learning that allows problem-solving through shared best practices [ 17 ]. ECHO India, in collaboration with National Health System Resource Centre (NHSRC), provided refresher training for ASHAs [ 18 ]. There is increasing evidence of the positive effect of ECHO training on medical provider’s learning and self-efficacy. However, its value as a training platform to CHWs in LMICs is limited. Previous studies that evaluated the use of the ECHO Model ™ for CHWs focussed on specific diseases and were conducted in high-income countries (HICs) [ 19 , 20 , 21 ]. For the adoption of digital technology, CHWs in LMICs encounter challenges such as poor proficiency levels in accessing and using digital platforms, limited access to troubleshooting, poor internet connectivity, and in-house support for resolving issues [ 22 ]. The present study was designed to assess the impact of the ECHO telementoring model for improving the knowledge and skills of ASHA workers in delivering comprehensive health services. This will provide new insights for measuring outcomes of digital training programs for CHWs (ASHA workers).

Study design

A pre–post quasi-experimental design using a convergent parallel mixed-method approach [ 23 ] was employed. The quantitative and qualitative data were collected concurrently. Inferences from both approaches were integrated during the reporting stage. This allowed for a comprehensive understanding of the effect of training on the knowledge and skills of ASHAs.

The ECHO training intervention and curriculum

Project ECHO ® designed a 15-h (over 6 months from October 2021 to March 2022), virtual, refresher training program to enhance the capacity of ASHAs to deliver counselling services for comprehensive healthcare in four states ( n  = 2293). Each session lasted for 90 min. The ECHO NHSRC training used a “hub and spoke” structure in which a multidisciplinary team of experts (trainers) based at a regional academic medical centre (the “hub”) engaged with the ASHAs (the “spokes”) [ 24 ] who attended the sessions from dedicated learning sites (PHCs). Each site also had a coordinator who would help facilitate the discussions and questions. The training curriculum was developed based on the NHSRC ‘ASHA training modules’ [ 18 ] in the regional languages in consultation with partners (hub-leaders and trainers). It comprised 10 sessions covering a range of topics, such as maternal health, new-born care, child health, nutrition, reproductive health, violence against women, tuberculosis, vector-borne diseases, non-communicable diseases, COVID-19, palliative care, and mental health. The training presentations included text with visual learning methods, such as images, videos, and links to training resources.

Study settings

The evaluation study was conducted in four states of India, where training sessions were held. These states represented the four geographical regions—northern (Himachal Pradesh) ( n =  499), southern (Tamil Nadu) ( n =  500), eastern (West Bengal) ( n =  618), and north-eastern (Sikkim) ( n =  676). The intervention (training sessions) was completed in March 2022. The end-point data were collected from March 2022 to May 2022.

Study participants and recruitment

Simple random sampling was used to select the ASHAs from each state for the quantitative survey. The participants were recruited from a list of ASHAs who would be receiving the ECHO NHSRC training. To be included, ASHAs had to be enrolled in the refresher training, planning to continue working for the next 10 months, with available contact details and consenting voluntarily. The ASHAs were contacted through mobile phones in each state. Key informant interviews (KIIs) were conducted with hub leaders who were involved in implementing the training, trainers (faculty) who delivered the lectures, and in-depth interviews (IDIs) with ASHAs.

Sample size

The sample size for the quantitative study was estimated by assuming a 25% improvement in knowledge and skills, 80% power, and a design effect factor of 1.7%. An adjustment of 30% loss to follow up and 20% non-response (from previous experience) led to a sample of 591 participants across four states, i.e., 148 participants from each state. For the qualitative study, purposive sampling with maximum variation across age, education, practice sites, and years of work experience was used for the selection of the participants. A total of 12 IDIs were conducted with ASHAs and nine KIIs with stakeholders (Additional file 2 : Appendix S2).

Study tools and data collection

For quantitative data collection, a structured questionnaire was designed through a collaborative approach with the research and program implementation team. The knowledge of ASHAs was assessed by a combination of 18 technical questions and case vignettes. Learning and competence, performance, and satisfaction were assessed with a 5-point Likert scale, using 1 = Strongly Disagree; 2 = Disagree; 3 = Neither Agree nor Disagree; 4 = Agree; and 5 = Strongly Agree. The face validity of the questionnaire was tested with ten ASHAs, separate from those recruited in the study and five primary care experts. The changes related to language, clarity, and relevance were made in the questionnaire based on the feedback from experts and participants. Separate discussion guides were developed for KIIs with trainers (Additional file 3 : Appendix S3) and hub-leaders (Additional file 4 : Appendix S4) and IDIs with ASHAs (Additional file 5 : Appendix S5). The guide focussed on examining the experience and practical applications of the training and was field tested before being administered in the main study. All study tools were translated into the local languages of the states and back-translated to check discrepancies.

The data were collected on the cell phone by experienced and trained researchers from social sciences backgrounds. Due to telephonic data collection, we were unable to capture non-verbal interview data such as emotions or gestures, particularly important in qualitative data. This may affect the richness of data and interpretation of responses. The quantitative tool was designed in the CS Pro software (version 7.5) and data were collected using its smartphone application. The qualitative interviews lasted around 40–50 min and were audio recorded. All interviews were translated and transcribed verbatim.

Data analysis

We summarized the quantitative data using descriptive statistics. Continuous variables were summarized using mean ± SD, and categorical variables were summarized using percentages and frequencies. The responses recorded using the 5-point Likert scale were recategorized during the analysis into three categories, i.e., ‘agree’ (combining strongly agree and agree), ‘disagree’ (combining strongly disagree and disagree), and ‘neutral [ 25 ]. Paired t test was used to find the difference between the pre- and post-scores of learning and competence and the attitude of participants toward ECHO training. McNemar’s test was used to assess changes in pre- and post-test scores for the technical domain. A p value of less than 0.05 was considered significant. STATA 16.0 statistical software was used for the analysis.

Qualitative data were analyzed according to the principles of the Framework approach [ 26 ], which combines inductive and deductive approaches. As a first step, two authors (SL and NM) familiarized themselves with four randomly selected transcripts and independently coded them using initial codes that were developed based on Moore’s framework levels of participation, satisfaction, learning, competence, and performance [ 27 ]. New codes that emerged while undertaking the analysis were included. The discussion and comparison of the double-coded transcripts enabled the development of an agreed set of codes. Any disagreements were discussed and resolved with the help of the third author (RP) to achieve inter-coder agreement. A final codebook was developed and applied to all the transcripts. The codes were combined and categorized into key emerging themes., The themes, including quotes (respondents’ exact words), were included to represent the main findings. Atlas.ti (version 8) software was used for data analysis.

Moore’s level 1—participation

Table 1 represents the baseline demographics of the recruited participants. From 610 participants who completed the pre-training survey, 490 participants completed the post-training survey, resulting in a follow-up rate of 80% (95% CI 76.6, 83.1). A total of 120 (20%, 95% CI 16.8, 23.3) participants were lost to follow up. This was due to a) contact numbers not being operational ( n =  96) and b) refusal due to time considerations ( n =  24). The field investigators attempted three additional phone calls, coordinated with hubs for participants’ alternate contact information, and offered flexible phone appointments to ensure maximum participation in the post-training survey. The majority (68%) of ASHAs were posted at sub-centres. A sub-centre is the most peripheral unit of contact of the health system with the community [ 28 ]. The majority of the participants (75%) had completed their high school (10th) education.

A hub leader described the efforts made by the ECHO to facilitate the participation of the ASHAs in the training.

“ECHO provided a facility where everyone can gather at the nearest block for the training. Physical and online modes [are] both available” (Hub-leader, Himachal Pradesh).

Moore’s level 2—satisfaction

The end-point survey assessed participants’ satisfaction with the ECHO training. The survey included eight items that measured overall training satisfaction and five items that measured satisfaction with factors specific to the telementoring model using close-ended questions. Satisfaction with the training content and environment was measured with four items. Except for one topic area (sharing of additional resources and training material), over 90% of participants were satisfied with almost all of the different components of the ECHO telementoring intervention (Additional file 1 : Appendix S1, Tables S1.1, S1.2, S1.3). While participants found the overall intervention favourable, 54.5% of all participants were dissatisfied with internet connectivity in the training sessions. Around one fourth of the participants faced challenges with the duration (31.2%), frequency (31.2%), and number of participants (28.4%) in the sessions (Additional file 1 : Appendix S1: Table S1.3).

The qualitative findings also show that most of the trainees were satisfied with the learning opportunity provided by the ECHO training.

“After attending these ECHO sessions, I felt we are constantly learning new techniques and it’s a deep sense of satisfaction” (ASHA, Tamil Nadu).

The ASHAs also shared areas or features of the ECHO model that did not meet their requirements and need improvement. They felt that the duration allotted for a session was not sufficient and some topics were covered very fast.

“They rush a lot while teaching over phone. It will be more helpful if they take more time and explain the things in a more detailed manner” (ASHA, WB)

Another ASHA suggested increasing the duration of training to improve their understanding of some topics.

"Increase the time of the training. Topics can be made deeper, and richer for better explanations" (ASHA, Tamil Nadu)

ASHAs described challenges related to connectivity while attending the training.

“The network connection was a problem and video used to lag” (ASHA, Sikkim)

Trainers shared their opinion about aspects of online trainings that did not meet their expectations.

“The problem is that they only join the meeting [online training] and do their own work, they actually do not listen properly.” (Trainer, WB)

A trainer mentioned that the large number of participants in some sessions affected the interaction among participant ASHAs.

“Sometimes a session has too many participants causing coordination efforts to be a challenge in these sessions” (Trainer, TN)

Difficulties in reaching the PHCs were recorded from the state of Sikkim. The geographical location and lack of transport facilities were mentioned by a trainer.

“We have transportation problem, our ASHA comes from rural area and it’s difficult to get taxi, which makes [it] harder to attend classes” (Trainer, Sikkim)

Many participants regarded organizational support as a facilitator for attending the training program. An ASHA from Tamil Nadu described how the issue of distance was resolved through management interventions from the organization.

“Our Block is 30 km away. There is another Block nearby that is 1 km only from here, they sent us there… so there was no problem” (ASHA, TN)

Moore’s level 3—learning

McNemar’s Chi-square statistic showed a significant difference between pre-ECHO and post-ECHO proportions in various aspects of health-related technical knowledge. Before the training, 1% of participants were aware of the correct schedule to be followed in the first week after the delivery of a child, which increased to 40% of participants post-training (p < 0.001). Overall, a statistically significant increase of 6% (95% CI 0.0003, 0.12; p =  0.038) in participants’ technical knowledge after ECHO training was found. After the training, a 7% increase in knowledge of malaria ( p =  0.002) and its symptoms and a 9% increase in knowledge of the right action to be undertaken (p < 0.001) was reported. Knowledge related to some areas such as recommended duration of physical activity or exercise (p < 0.001), immunisation after child birth ( p =  0.001), family planning in women after child birth ( p =  0.002) showed a decrease after attending the training (Additional file 1 : Appendix S1, Table S2). Post ECHO training, ASHAs reported an improvement in their knowledge of using a smartphone (switch on and off, and navigate) ( p =  0.0005) and navigating a mobile application ( p =  0.59). The ASHAs reported a 2% decrease in their knowledge of downloading content in the mobile ( p =  0.07) (Fig.  1 ).

Self-rated ICT knowledge of ASHAs

The qualitative data show that ASHAs who did not have a smartphone found it difficult to download and save content. One of the participants reported receiving additional training content in the form of a pdf file. She also mentioned that those who do not use a smartphone find it challenging to access this additional resource.

“We get the study material in a pdf so that simplifies our work further. But those who do not have a smartphone, find it difficult to get this opportunity” (ASHA, WB)

3A—Declarative learning

Declarative learning assesses how participants articulate the knowledge that the educational activity intended them to know (knowing what). The qualitative findings show that the training had increased the ASHA’s knowledge in specific domains such as breastfeeding during COVID-19.

“The doubt was whether a mother can breastfeed the baby when suffering from COVID-19. I got clarity about that… many such topics were cleared” (ASHA, Himachal Pradesh)

3B—Procedural learning

Procedural learning assesses the participants' articulation of how to do what the educational activity intended them to know (knowing how).

Participants reported that they had gained new skills related to the approach and identification of healthcare issues after attending the ECHO training.

“Earlier we wouldn’t know if ear related issues had a resolution – But following the ear related training we are aware that such issues can be cured or have treatments” (ASHA, Tamil Nadu).

The qualitative interviews revealed additional themes that described the value of the ECHO training program in improving the learning experience of ASHAs.

ASHA workers felt that the case presentations from their peers enhanced their learning experience.

“One ASHA shared a case of an anaemic mother. Based on this case we learned that this could have been prevented if iron tablets are provided from the adolescent stage” (ASHA, Tamil Nadu).

The interactive nature of the sessions and the discussions benefitted the learning experience of the ASHAs.

“Open discussion helped us so much. We can discuss any topics if we haven’t understood and sir used to explain again” (ASHA, Sikkim)

Moore’s level 4—competence

The participants reported significant improvement in their confidence to identify and manage several health conditions like birth asphyxia (for home deliveries) and management with a mucus extractor ( p =  0.01), screen and refer pregnant women ( p =  0.01), disseminate information on domestic violence and sexual harassment ( p =  0.001). Overall, a statistically significant increase of 6% (95% CI 0.01, 0.10; p =  0.01) in participants’ competence after attending the ECHO training was found. Participants reported a decrease in their confidence to track child immunisation ( p  < 0.001), monitor symptoms of COVID (p < 0.001), and clarify concerns of the community ( p  < 0.001) after attending the training (Additional file 1 : Appendix S1, Table S3).

Participants mentioned an improvement in their confidence while communicating with patients and their families.

“Initially we could not talk to people so comfortably, we hesitated at times but after being trained we can talk to people and their families properly and easily now” (ASHA, West Bengal)

An ASHA described a gap in their ability to talk to mothers in the field and suggested including more training content on efficient communication skills.

“We go on field and talk to mothers. There was no training for these, but I feel it will be good if we can have training on how to talk to mothers comfortably” (ASHA, WB)

Moore’s level 5—performance

The study identified a significant improvement in ASHAs’ positive attitude toward maternal and child health issues. Overall, a 5% improvement (95% CI − 0.009, 0.10; p value = 0.09) in participants’ attitudes post-ECHO training was found. Almost all the participants (99%) reported applying the skills learnt during the training at their workplaces. More than 90% of the participants felt that the ECHO training expanded access to healthcare in their community (Fig.  2 ). The ASHAs reported an improvement in their attitude towards inclusion of HIV patients in the community ( p =  0.01) and home visits for new born babies (p < 0.001) (Additional file 1 : Appendix S1, Table S4).

Self-reported performance of ASHAs

The ASHAs shared specific examples where they made changes in their practice or treatment strategies after attending the training.

“[Earlier] the implementation was not proper [correct]. As an example, if a child’s life has to be saved on the spot, we would take the medicines and syringes separately. Now we take the necessary items section wise including the AFI kit. So that’s the change” (ASHA, Tamil Nadu).

The results of this evaluation suggest that Project ECHO provides a suitable and efficacious platform for training for ASHAs. The participants reported an improvement in their knowledge, skills, and practices. They also described improved confidence to communicate more effectively. Some areas in which the ASHAs reported a lack of knowledge and confidence include newborn immunisation and family planning after pregnancy.

The NRHM guidelines for the recruitment of ASHAs require candidates to have at least eight or 10 completed years of formal education. Low literacy and inadequate training of ASHAs have been observed in different states in India [ 30 , 31 ]. However, with the proper training and support, ASHAs can provide comprehensive preventive and promotive healthcare services [ 29 ]. In this study, the majority (75%) of ASHAs across all states had ten or more years of schooling. The ECHO training will bolster their knowledge, skills, and confidence in providing effective services.

The ASHAs receive 23 days of training in the first year, followed by 12 days of training in every subsequent year to keep them updated with the knowledge and skills needed to effectively perform their roles and responsibilities. Previous studies have identified many challenges in the training of ASHAs, such as lack of regular refresher training [ 32 ], shortage of competent trainers, insufficient funds, and use of obsolete health information [ 33 ]. The training programs have mostly been didactic-based and had limitations in the engagement of participants [ 34 ]. The ECHO NHSRC refresher training addresses these limitations by promoting peer-to-peer learning and through a case-based discussion approach [ 35 ].

Our findings report a significant increase in the knowledge of ASHA workers with respect to specific domains like maternal and child health. A randomized controlled trial in Karnataka, India, found a significant improvement in mental health knowledge, attitude, and practice (KAP) scores amongst ASHAs trained by a hybrid training (traditional 1-day in-person classroom training and seven online sessions using the ECHO Model) against conventional classroom training [ 14 ]. This study findings highlight the improvement in knowledge of ASHAs related to oral health and palliative care post-ECHO training. An improvement in knowledge has also been observed in other studies that have evaluated ECHO telementoring interventions in cancer screening [ 36 ], palliative care [ 37 , 38 ], HIV [ 39 ], and chronic pain [ 40 ] In this study, ASHAs reported poor knowledge of the immunisation schedule for a newborn as well as the confidence to record and track immunisation in the community even after the ECHO training. A critical function of ASHAs is to assist ANMs or nurses with all immunisation activities [ 41 ]. A previous study in Karnataka in 2020 found inadequate knowledge among ASHAs about child immunisation. The above study also documented that by increasing the number of days and focusing on child care the ASHAs had a better understanding of interventions related to child healthcare [ 42 ]. As a part of the course structure, ECHO provides one session on new born and post-partum care. An assessment of the number of sessions needed to cover the topics was beyond the scope of our research but would be beneficial.

Previous studies have identified several shortcomings in ASHAs' communication and counselling abilities [ 43 , 44 , 45 ]. The findings of this study revealed that the ASHAs faced communication issues while discussing health matters related to family planning and COVID-19 with the community. Previous research has found that interpersonal communication of ASHAs are influenced by factors such as health system support and community context [ 46 ]. A study exploring the perspectives of ASHAs on a mobile training course in India also found that they encountered barriers in their interactions with beneficiaries such as resistance from family members, fear of poor quality of care, and financial costs of care [ 44 ]. Training programs must therefore, also incorporate how ASHAs can navigate social behaviours and norms to improve the impact of counselling [ 47 , 48 ]. The extent to which the ECHO training can identify and incorporate community hierarchies to improve communication of the ASHAs needs further exploration. In this study, large batch size ( n =  40) and limited use of video by participants during the training hampered the engagement between ASHAs as well as with the trainers. A previous study in the USA suggested that limiting batch size and ensuring face-to-face interactions on the virtual platform ensured a higher level of accountability and made it easier to engage with others in the ECHO training sessions [ 49 ].

CHWs face significant barriers when using digital technology in LMICs, making it challenging for them to access training on digital platforms [ 50 ]. The ASHAs in this study reported an improvement in their ability to use smartphones and navigate mobile applications. Our findings also suggest that ASHAs should be better oriented for accessing content on hand held devices.

The mentorship by trainers added value to participants’ knowledge and helped improve their skills. In this study, participants’ attitudes towards their work changed after attending the ECHO training suggesting that the learning and confidence developed during the training would be transferable to their work in healthcare settings and communities. The ECHO participants of previous studies have also demonstrated similar changes in their practices [ 35 , 40 ]. Our study findings indicate that the ECHO Model is an effective platform that can help foster a virtual community of practice through case-based learning, shared best practices, and online mentorship by experts.

Future directions

There should be more sessions on topics related to post-natal and newborn care as the ASHAs showed poor knowledge and competence in these areas.

There should be more training on counselling and development of communication skills for ASHAs, specially for maternal and child health and COVID-19.

An orientation for ASHAs should be conducted to facilitate the use of technology and the platform for learning. This may also help overcome some of the challenges described by the ASHAs in this study.

Strengths and limitations

The study used a rigorous quasi-experimental design across four different states of India. Our follow-up rate in the study was 80%, indicating a high response from participants completing the pre–post assessment. The presented study has certain limitations. It was not possible to use randomisation and a pure experimental design in this study, and this affects the internal validity of the study. The inclusion of a control group would have strengthened study validity. The self-reported outcomes can be subject to social desirability bias. We did not document the information on attendance and drop outs from the training program. The qualitative results have to be carefully interpreted because of the small sample size of the qualitative study relative to the study sample.

There is increasing recognition of the importance of CHWs globally for promoting a continuum of care and expanding access to health services. ASHA workers constitute critical human resources in the Indian health system and efforts to empower them are crucial for strengthening the health system at the community level. The encouraging results of this study indicate the effectiveness of Project ECHO in building the capacity of ASHA workers across different geographies in the country.

Availability of data and materials

All data generated or analyzed during this study are included in this published article (as Additional files).

Abbreviations

Community health workers

Sustainable development goals

National Rural Health Mission

Accredited social health activists

Digital infrastructure knowledge sharing

Ministry of Human Resource Development

Coronavirus Disease 2019

National Health System Resource Centre

World Health Organization

High-Income Countries

LMICs: Low- and Middle-Income Countries

Extension for Community Healthcare Outcomes

In-depth interviews

Key informant interviews

Continuing medical education

Institutional Ethics Committee

Participant Information Sheet

Jodhpur School of Public Health

Public Health Foundation of India

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Acknowledgements

The authors wish to thank all the healthcare workers who kindly participated in this study giving their time, experience, and insights. We also thank Dr. Sourabh Chakraborty (Professor, JSPH), Mr. Swapnil Gupta, and the JSPH data collection team for their contribution to the collection of good quality data in a short time.

The study was funded by Extension for Community Healthcare Outcomes (ECHO) India.

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Contributions

R.M. contributed to the conception and design of the study and significant inputs for data analysis and made a significant contribution to the drafting of the discussion and conclusion of the paper. S.L. wrote the first draft of the manuscript. N.M. and S.L. contributed to the implementation of the study and development of interview guides, analysis, and validation of qualitative data. R.P. and K.S. contributed to the analysis and validation of quantitative data. R.M., N.M., R.P., K.S, A.K.R. and K.R. reviewed the manuscript and gave significant inputs for improving the paper. All authors read and approved the final manuscript.

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Correspondence to Supriya Lahoti .

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Ethical clearance was received from the Institutional Ethical Committee (IEC) of the Public Health Foundation of India (PHFI) (ref: TRC-IEC 472/21, dated 26 August 2021). The study has also been registered at the Clinical Trials Registry, India (CTRI/2021/10/037189). All methods were performed in accordance with the relevant guidelines and regulations. A written Participant Information Sheet (PIS) and informed consent form was provided to the participants before conducting the interviews. Verbal informed consent was taken from all participants, and the process of verbal informed consent was approved by the ethics committee (Institutional Ethics Committee (IEC) of the PHFI). Data confidentiality was maintained by coding with the unique identification (ID) of all the participants. The interviews were audio-recorded, and audio files and transcripts were kept in a password-protected folder.

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Supplementary Information

Additional file 1.

: Appendix S1. Table S1.1. Satisfaction with different factors of the training. Table S1.2. Satisfaction with content and environment of the training. Table S1.3. Challenges faced with respect to ECHO tele-mentoring model. Table S2. Technical knowledge and skills. Table S3. Statements assessing competence. Table S4. Statements assessing attitude and performance.

Additional file 2

: Appendix S2. Participants in qualitative interviews.

Additional file 3

: Appendix S3. Key informant Interview Guide for Trainers End line Evaluation.

Additional file 4

: Appendix S4. Key informant interview guide for Hub leaders End line Evaluation.

Additional file 5

: Appendix S5. In-depth Interview Guide for ASHAs End line Evaluation.

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Panda, R., Lahoti, S., Mishra, N. et al. A mixed methods evaluation of the impact of ECHO ® telementoring model for capacity building of community health workers in India. Hum Resour Health 22 , 26 (2024). https://doi.org/10.1186/s12960-024-00907-y

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• Community health workers (CHWs)
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• Maternal and child health
• Primary healthcare
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• ECHO telementoring
• Mixed-method study

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Scenario analysis of local storylines to represent uncertainty in complex human-water systems

• Alizadeh, Mohammad Reza
• Adamowski, Jan
• Inam, Azhar

Storylines are important in evaluating the uncertainty inherent in complex human-water systems. The interrelated nature of qualitative and quantitative scenarios can enhance our ability to address the uncertainty of integrated modelling of complex systems. This study proposes a transdisciplinary approach that integrates social and environmental sciences to characterize and comprehend uncertainty in the dynamic interactions of key factors affecting a human-water system. We introduce a framework for representing uncertainty through linguistic and epistemic uncertainty quantification using storyline narratives in the context of a regional integrated dynamic model. A systematic exploration of uncertainty space is performed using storytelling, fuzzy sets, and low discrepancy sequences sampling methods. Scenario analysis is applied to the generated uncertain ensemble of projections to discover predominant storylines of interest. As a representative case of a human-water system operating in a developing country, we examine the uncertainty effects of a variety of drivers of climatic and socio-economic changes on key agriculture and water-related sectors in Pakistan's Rechna Doab region. The findings revealed soil salinity and crop yield indices were the most uncertain and showed significant variance across all developed storylines. The 95th percentile for soil salinity in year 2100 was estimated to be nearly 60 % higher than the baseline level (year 2020). There was, however, considerable overlap in different socio-economic scenarios at the local scale, indicating that change in socio-economic conditions could not fully offset climate-related uncertainty. Our analysis provides better quantification and a deeper understanding of the uncertainty in integrated assessment modelling of coupled human-water systems and the complex relationships between inputs and outcomes.

• Scenario analysis;
• Uncertainty;
• Integrated dynamic models;
• Storyline narratives;
• Human-water systems;
• Participatory modelling

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This first-of-its-kind Global Medical Physics Training and Development Program (GMPTDP) seeks to serve as an opportunity for PSOM and SEAS graduate students to enhance their clinical requirement with a global experience, introduce them to global career opportunities and working effectively in different contexts, and strengthens partnerships for education and research between US and Africa. This would also be an exceptional opportunity for pre-med/pre-health students and students interested in health tech to have a hands-on global experience with some of the leading professionals in the field. The project will include instruction in automated radiation planning through artificial intelligence (AI); this will increase access to quality cancer care by standardizing radiation planning to reduce inter-user variability and error, decreasing workload on the limited radiation workforce, and shortening time to treatment for patients. GMPTDP will offer a summer clinical practicum to Penn students during which time they will also collaborate with UGhana to implement and evaluate AI tools in the clinical workflow.

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In collaboration with UNHCR, we propose studying the effects of a dramatic drop in the level of support for refugees, using a regression discontinuity design to survey 2,500 refugee households just above and 2,500 households just below the vulnerability score cutoff that determines eligibility for full rations. This study will identify the effects of aid cuts on the welfare of an important marginalized population, and on their livelihood adaptation strategies. As UNHCR faces budgetary cuts in multiple refugee-hosting contexts, our study will inform policymakers on the effects of funding withdrawal as well as contribute to the literature on cash transfers.

The proposed project, titled "A History of Regenerative Agriculture Practices from the Global South: Case Studies from Ethiopia, Kenya, and Zimbabwe," aims to delve into the historical and contemporary practices of regenerative agriculture in sub-Saharan Africa. Anticipated Outputs and Outcomes:

1. Research Paper: The primary output of this project will be a comprehensive research paper. This paper will draw from a rich pool of historical and contemporary data to explore the history of regenerative agriculture practices in Ethiopia, Kenya, and Zimbabwe. It will document the indigenous knowledge and practices that have sustained these regions for generations.

2. Policy Digest: In addition to academic research, the project will produce a policy digest. This digest will distill the research findings into actionable insights for policymakers, both at the national and international levels. It will highlight the benefits of regenerative agriculture and provide recommendations for policy frameworks that encourage its adoption.

3. Long-term Partnerships: The project intends to establish long-term partnerships with local and regional universities, such as Great Lakes University Kisumu, Kenya. These partnerships will facilitate knowledge exchange, collaborative research, and capacity building in regenerative agriculture practices. Such collaborations align with Penn Global's goal of strengthening institutional relationships with African partners.

The Penn Computerized Neurocognitive Battery (PCNB) was developed at the University of Pennsylvania by Dr. Ruben C. Gur and colleagues to be administered as part of a comprehensive neuropsychiatric assessment. Consisting of a series of cognitive tasks that help identify individuals’ cognitive strengths and weaknesses, it has recently been culturally adapted and validated by our team for assessment of school-aged children in Botswana . The project involves partnership with the Botswana Ministry of Education and Skills Development (MoESD) to support the rollout of the PCNB for assessment of public primary and secondary school students in Botswana. The multidisciplinary Penn-based team will work with partners in Botswana to guide the PCNB rollout, evaluate fidelity to the testing standards, and track student progress after assessment and intervention. The proposed project will strengthen a well-established partnership between Drs. Elizabeth Lowenthal and J. Cobb Scott from the PSOM and in-country partners. Dr. Sharon Wolf, from Penn’s Graduate School of Education, is an expert in child development who has done extensive work with the Ministry of Education in Ghana to support improvements in early childhood education programs. She is joining the team to provide the necessary interdisciplinary perspective to help guide interventions and evaluations accompanying this new use of the PCNB to support this key program in Africa.

This project will build on exploratory research completed by December 24, 2023 in which the PI interviewed about 35 South Africans involved in jazz/improvised music mostly in Cape Town: venue owners, curators, creators, improvisers.

• Podcast series with 75-100 South African musicians interviewed with their music interspersed in the program.
• 59 minute radio program with extended excerpts of music inserted into the interview itself.
• Create a center of knowledge about South African jazz—its sound and its stories—building knowledge globally about this significant diasporic jazz community
• Expand understanding of “jazz” into a more diffuse area of improvised music making that includes a wide range of contemporary indigenous music and art making
• Partner w Lincoln Center Jazz (and South African Tourism) to host South Africans at Penn

This study focuses on the potential of a Megaregional approach for fostering sustainable development, economic growth, and social inclusion within the East African Community (EAC), with a specific focus on supporting the development of A Vision for An Inclusive Joint Lakefront across the 5 riparian counties in Kenya.

By leveraging the principles of Megaregion development, this project aims to create a unified socio-economic, planning, urbanism, cultural, and preservation strategy that transcends county boundaries and promotes collaboration further afield, among the EAC member countries surrounding the Lake Victoria Basin.

Anticipated Outputs and Outcomes:

1. Megaregion Conceptual Framework: The project will develop a comprehensive Megaregion Conceptual Framework for the Joint Lakefront region in East Africa. This framework, which different regions around the world have applied as a way of bridging local boundaries toward a unified regional vision will give the Kisumu Lake region a path toward cooperative, multi-jurisdictional planning. The Conceptual Framework will be both broad and specific, including actionable strategies, projects, and initiatives aimed at sustainable development, economic growth, social inclusion, and environmental stewardship.

2. Urbanism Projects: Specific urbanism projects will be proposed for key urban centers within the Kenyan riparian counties. These projects will serve as tangible examples of potential improvements and catalysts for broader development efforts.

3. Research Publication: The findings of the study will be captured in a research publication, contributing to academic discourse and increasing Penn's visibility in the field of African urbanism and sustainable development

Antimicrobial resistance (AMR) has emerged as a global crisis, causing more deaths than HIV/AIDS and malaria worldwide. By engaging in a collaborative effort with the Botswana Ministry of Health’s data scientists and experts in microbiology, human and veterinary medicine, and bioinformatics, we will aim to design new electronic medical record system modules that will:

Aim 1: Support the capturing, reporting, and submission of microbiology data from sentinel surveillance laboratories as well as pharmacies across the country

Aim 2: Develop data analytic dashboards for visualizing and characterizing regional AMR and AMC patterns

Aim 3: Submit AMR and AMC data to regional and global surveillance programs

Aim 4: Establish thresholds for alert notifications when disease activity exceeds expected incidence to serve as an early warning system for outbreak detection.

  Using a novel interdisciplinary approach that bridges development economics, psychology, and neuroscience, the overall goal of this project is to improve children's development using a poverty-reduction intervention in Cote d'Ivoire (CIV). The project will directly measure the impacts of cash transfers (CTs) on neurocognitive development, providing a greater understanding of how economic interventions can support the eradication of poverty and ensure that all children flourish and realize their full potential. The project will examine causal mechanisms by which CTs support children’s healthy neurocognitive development and learning outcomes through the novel use of an advanced neuroimaging tool, functional Near Infrared Spectroscopy (fNIRS), direct child assessments, and parent interviews.

The proposed research, the GIGA initiative for Improving Education in Rwanda (GIER), will produce empirical evidence on the impact of connecting schools on education outcomes to enable Rwanda to better understand how to accelerate the efforts to bring connectivity to schools, how to improve instruction and learning among both teachers and students, and whether schools can become internet hubs capable of providing access e-commerce and e-government services to surrounding communities. In addition to evaluating the impact of connecting schools on educational outcomes, the research would also help determine which aspects of the program are critical to success before it is rolled out nationwide.

Through historical epigraphic research, the project will test the hypothesis that historical processes and outcomes in the 14th century were precipitated by a series of related global and local factors and that, moreover, an interdisciplinary and synergistic analysis of these factors embracing climatology, hydrology, epidemiology linguistics and migration will explain the transformation of the cultural, religious and social landscapes of the time more effectively than the ‘clash of civilizations’ paradigm dominant in the field. Outputs include a public online interface for the epigraphic archive; a major international conference at Penn with colleagues from partner universities (Ghent, Pisa, Edinburgh and Penn) as well as the wider South Asia community; development of a graduate course around the research project, on multi-disciplinary approaches to the problem of Hindu-Muslim interaction in medieval India; and a public facing presentation of our findings and methods to demonstrate the path forward for Indian history. Several Penn students, including a postdoc, will be actively engaged.  

India’s competitive electoral arena has failed to generate democratic accountability pressures to reduce toxic air. This project seeks to broadly understand barriers to such pressures from developing, and how to overcome them. In doing so, the project will provide the first systematic study of attitudes and behaviors of citizens and elected officials regarding air pollution in India. The project will 1) conduct in-depth interviews with elected local officials in Delhi, and a large-scale survey of elected officials in seven Indian states affected by air pollution, and 2) partner with relevant civil society organizations, international bodies like the United Nations Environment Program (UNEP), domain experts at research centers like the Public Health Foundation of India (PHFI), and local civic organizations (Janagraaha) to evaluate a range of potential strategies to address pollution apathy, including public information campaigns with highly affected citizens (PHFI), and local pollution reports for policymakers (Janagraaha).

The biggest benefit from generative AI such as GPT, will be the widespread availability of tutoring systems to support education. The project will use this technology to build a conversational voicebot to support Indian students in learning English. The project will engage end users (Indian tutors and their students) in the project from the beginning. The initial prototype voice-driven conversational system will be field-tested in Indian schools and adapted. The project includes 3 stages of development:

1) Develop our conversational agent. Specify the exact initial use case and Conduct preliminary user testing.

2) Fully localize to India, addressing issues identified in Phase 1 user testing.

3) Do comprehensive user testing with detailed observation of 8-12 students using the agent for multiple months; conduct additional assessments of other stakeholders.

The project partners with Ashoka University and Pratham over all three stages, including writing scholarly papers.

Through empirical policy analysis and data-based scenario planning, this project actively contributes to this global effort by investigating planning and policy responses to autonomous transportation in the US and China. In addition to publishing several research papers on this subject, the PI plans to develop a new course and organize a forum at PWCC in 2025. These initiatives are aligned with an overarching endeavor that the PI leads at the Weitzman School of Design, which aims to establish a Future Cities Lab dedicated to research and collaboration in the pursuit of sustainable cities.

This study aims to fill this gap through a more humanistic approach to measuring the impact of education on national development. Leveraging a mixed methods research design consisting of analysis of quantitative data for trends over time, observations of schools and classrooms, and qualitative inquiry via talking to people and hearing their stories, we hope to build a comprehensive picture of educational trends in Nepal and their association with intra-country development. Through this project we strive to better inform the efforts of state authorities and international organizations working to enhance sustainable development within Nepal, while concurrently creating space and guidance for further impact analyses. Among various methods of dissemination of the study’s findings, one key goal is to feed this information into writing a book on this topic.

Developing cities across the world have taken the lead in adopting local environmental regulation. Yet standard models of environmental governance begin with the assumption that local actors should have no incentives for protecting “the commons.” Given the benefits of climate change regulation are diffuse, individual local actors face a collective action problem. This project explores why some local governments bear the costs of environmental regulation while most choose to free-ride. The anticipated outputs of the project include qualitative data that illuminate case studies and the coding of quantitative spatial data sets for studying urban land-use. These different forms of data collection will allow me to develop and test a theoretical framework for understanding when and why city governments adopt environmental policy.

The proposed project will develop new insights on the issue of legislative solutions to the nurse staffing crisis, which will pertain to many U.S. states and U.K. countries. The PI will supervise the nurse survey data collection and to meet with government and nursing association stakeholders to plan the optimal preparation of reports and dissemination of results. The anticipated outputs of the project are a description of variation throughout Scotland in hospital nursing features, including nurse staffing, nurse work environments, extent of adherence to the Law’s required principles, duties, and method, and nurse intent to leave. The outcomes will be the development of capacity for sophisticated quantitative research by Scottish investigators, where such skills are greatly needed but lacking.  

The proposed project will engage multi-cohort, cross-national comparisons of educational-attainment and labor-market experiences of young adults in three countries that dramatically diverge in how they have developed college education over the last three decades: Finland, South Korea and the US. It will produce comparative knowledge regarding consequences of different pathways to higher education, which has significant policy implications for educational and economic inequality in Finland, Korea, the US, and beyond. The project also will lay the foundation for ongoing collaboration among the three country teams to seek external funding for sustained collaboration on educational analyses.

With matching funds from PLAC and CLALS, we will jointly fund four scholars from diverse LAC countries to participate in workshops to engage our community regarding successful practices of community-academic partnerships.

These four scholars and practitioners from Latin America, who are experts on community-engaged scholarship, will visit the Penn campus during the early fall of 2024. As part of their various engagements on campus, these scholars will participate after the workshops as key guest speakers in the 7th edition of the Penn in Latin America and the Caribbean (PLAC) Conference, held on October 11, 2024, at the Perry World House. The conference will focus on "Public and Community Engaged Scholarship in Latin America, the Caribbean, and their Diasporas."

Palermo, Sicily, has been a leading center of migrant rights advocacy and migrant civic participation in the twenty-first century. This project will engage an existing network of diverse migrant community associations and anti-mafia organizations in Palermo to take stock of migrant rights and support systems in the city. Our partner organizations, research assistants, and cultural mediators from different communities will design and conduct a survey and interviews documenting experiences, issues and opportunities related to various rights – to asylum, housing, work, health care, food, education, and more. Our web-based report will include recommendations for city and regional authorities and other actors in civil society. The last phase of our project will involve community outreach and organizing to advance these objectives. The web site we create will be designed as the network’s information center, with a directory of civil society and services, updating an inventory not current since 2014, which our partner Diaspore per la Pace will continue to update.

This interdisciplinary project has four objectives: 1) to investigate why some governments and non-state actors elevated cultural heritage exploitation (CHX) to the strategic level of warfare alongside nuclear weapons, cyberattacks, political influence operations and other “game changers”; 2) which state or non-state actors (e.g. weak actors) use heritage for leverage in conflict and why; and 3) to identify the mechanisms through which CHX coerces an adversary (e.g. catalyzing international involvement); and 4) to identify the best policy responses for non-state actors and states to address the challenge of CHX posed by their adversaries, based on the findings produced by the first three objectives.

Identify the capacity of dental schools, organizations training oral health professionals and conducting oral health research to contribute to oral health policies in the WHO Eastern Mediterranean region, identify the barriers and facilitators to engage in OHPs, and subsequently define research priority areas for the region in collaboration with the WHO, oral health academia, researchers, and other regional stakeholders.

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

Affiliations

Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
• Criterion sampling-selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling-selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.  Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

• Internal validity--- Credibility
• External validity---Transferability
• Reliability---Dependability
• Objectivity---Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.  

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
• Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

• Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

• Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research.  Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc.  Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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