qualitative research recent developments in case study methods

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Qualitative research: Recent developments in case study methods

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This article surveys the extensive new literature that has brought about a renaissance of qualitative methods in political science over the past decade. It reviews this literature's focus on causal mechanisms and its emphasis on process tracing, a key form of within-case analysis, and it discusses the ways in which case-selection criteria in qualitative research differ from those in statistical research. Next, the article assesses how process tracing and typological theorizing help address forms of complexity, such as path dependence and interaction effects. The article then addresses the method of fuzzy-set analysis. The article concludes with a call for greater attention to means of combining alternative methodological approaches in research projects.

• Path dependence
• Process tracing
• Selection bias

ASJC Scopus subject areas

• Sociology and Political Science

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• 10.1146/annurev.polisci.8.082103.104918

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• Link to publication in Scopus
• Link to the citations in Scopus

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T1 - Qualitative research

T2 - Recent developments in case study methods

AU - Bennett, Andrew

AU - Elman, Colin

N2 - This article surveys the extensive new literature that has brought about a renaissance of qualitative methods in political science over the past decade. It reviews this literature's focus on causal mechanisms and its emphasis on process tracing, a key form of within-case analysis, and it discusses the ways in which case-selection criteria in qualitative research differ from those in statistical research. Next, the article assesses how process tracing and typological theorizing help address forms of complexity, such as path dependence and interaction effects. The article then addresses the method of fuzzy-set analysis. The article concludes with a call for greater attention to means of combining alternative methodological approaches in research projects.

AB - This article surveys the extensive new literature that has brought about a renaissance of qualitative methods in political science over the past decade. It reviews this literature's focus on causal mechanisms and its emphasis on process tracing, a key form of within-case analysis, and it discusses the ways in which case-selection criteria in qualitative research differ from those in statistical research. Next, the article assesses how process tracing and typological theorizing help address forms of complexity, such as path dependence and interaction effects. The article then addresses the method of fuzzy-set analysis. The article concludes with a call for greater attention to means of combining alternative methodological approaches in research projects.

KW - Fuzzy set

KW - Path dependence

KW - Process tracing

KW - Selection bias

KW - Typologies

UR - http://www.scopus.com/inward/record.url?scp=33745403794&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745403794&partnerID=8YFLogxK

U2 - 10.1146/annurev.polisci.8.082103.104918

DO - 10.1146/annurev.polisci.8.082103.104918

M3 - Review article

AN - SCOPUS:33745403794

SN - 1094-2939

JO - Annual Review of Political Science

JF - Annual Review of Political Science

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29 Case Study Methods

Andrew Bennett is Professor of Government at Georgetown University.

Colin Elman is Associate Professor of Political Science, The Maxwell School, Syracuse University.

• Published: 02 September 2009
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This article focuses on a third generation of qualitative methods research. Third-generation qualitative methods provide a unique bridge between the single-logic-of-inference and interpretivist communities. Accepting comparison and intuitive regression as part of its underlying justification, the third-generation case study approach is readily compatible with large-n studies, as well as being accepting of many of the claims of the comparative advantages offered by quantitative methods. The article considers some of the ways in which the third generation has developed and suggest potentially fruitful directions for future research. It focuses on some key innovations in third-generation qualitative methods over the last decade regarding within-case analysis, comparative case studies, case selection, concepts and measurement, counterfactual analysis, typological theorizing, and Fuzzy Set analysis. It concludes with a discussion of promising avenues for future developments in qualitative methods.

As we have noted elsewhere ( Bennett and Elman 2007 a ), qualitative research methods are currently enjoying an almost unprecedented popularity and vitality in both the international relations and the comparative politics subfields. To be sure, this renaissance has not been fully reflected in contemporary studies of American politics, which continue to emphasize statistical analysis and formal modeling ( Bennett, Barth, and Rutherford 2003 ; Bennett and Elman 2007 b ; Mahoney 2007 ; Pierson 2007 ). Nevertheless, in the international relations subfield, qualitative methods are indisputably prominent, if not pre‐eminent. In a 2007 survey, 95 percent of US international relations scholars reported that qualitative analysis is their primary or secondary methodology, compared with 55 percent for quantitative analysis and 16 percent for formal modeling ( Maliniak et al. 2007, 37 ). Substantively, qualitative research has contributed to essentially every research program in international relations, including those on international political economy ( Odell 2004 ), the democratic peace ( George and Bennett 2005, 37–59 ), ethnic and civil conflicts ( Sambanis 2004 ; Bennett and Elman 2007 b ), the end of the cold war ( Wohlforth 1998 ), international environmental politics ( Mitchell and Bernauer 2004 ), and security studies ( Katzenstein 1996 ; Kacowicz 2004 ).

The present resurgence in qualitative research methods owes a significant debt to two previous generations of ground‐breaking scholars. A first generation of post‐Second World War case study methods made important contributions but eventually came to be seen as being too atheoretical, lacking in methodological rigor, and not conducive to cumulative theory‐building ( George 1979 ). A second generation of scholars developed more systematic procedures for qualitative research from the 1970s through the early 1990s, including Adam Przeworski and Henry Teune (1970) , Arend Lijphart (1971) , Harry Eckstein (1975) , Neil Smelser (1976) , Alexander George (1979) , Timothy McKeown ( George and McKeown 1985 ), Charles Ragin (1987) , and David Collier (1993) .

We focus in this chapter on a third generation of qualitative methods research. We argue that this third generation constitutes a renaissance in qualitative methods that has clarified their procedures, grounded them more firmly in the contemporary philosophy of science, illuminated their comparative advantages relative to quantitative and formal methods, and expanded the repertoire of qualitative techniques on conceptualization and measurement, case selection, and comparative and within‐case analysis. Qualitative methods have also become more deeply institutionalized than in prior periods. In 2003, the American Political Science Association (APSA) formed the Qualitative Methods section. Subsequently renamed Qualitative and Multi‐Method Research, as of February 2008 it was the second‐largest of APSA's thirty‐seven sections. In addition, the Consortium for Qualitative Research Methods (CQRM) was founded in 2001, and in 2007–8 it had roughly sixty member departments and research institutes. By January 2008 it had co‐organized seven Institutes for Qualitative and Multi‐Method Research, training more than 600 graduate students and faculty in state‐of‐the‐art qualitative methods ( Collier and Elman 2008 ). 1

An important dimension of the recent development of qualitative research methods has been the emergence of more pluralistic attitudes toward methodology. This pluralism is clearest in the increasing use of sophisticated multi‐method research designs. It is also evident within the qualitative research methods community itself, which has grown in size and variety to the point that it now incorporates a diverse range of views. One school of thought, influenced by quantitative research techniques, suggests that “the same underlying logic provides the framework for each research approach [statistical and qualitative]. This logic tends to be explicated and formalized clearly in discussions of quantitative research methods” ( King, Keohane, and Verba 1994, 3 ). In contrast to this single‐logic‐of‐inference approach, advocates of what we have termed the third generation of qualitative research argue that there are alternative ways to make inferences. These scholars often use within‐case methods to establish the presence of particular causal mechanisms and the conditions under which they operate. 2 The third‐generation group also emphasizes the value of theory development, as well as theory testing; and of historical explanation of individual cases, as well as generalized statements about causal mechanisms. In the view of third‐generation qualitative methodologists, different methods have different strengths and weaknesses, and they should be used with those trade‐offs in mind. From this perspective, quantitative methods are very useful and powerful, but they are not always the best choice for all inferential goals, even when many cases exist.

The qualitative research methods community also includes a variety of scholars engaged in interpretative approaches. Interpretivism involves an extraordinary range of modes of analysis, 3 as well as difficult issues arising from the variety of philosophical and linguistic traditions in which interpretative approaches have their roots. We find useful in the international relations subfield John Ruggie's distinction (1998) among neoclassical, postmodernist, and naturalistic constructivists. Neoclassical constructivists, among whom Ruggie counts himself, follow a pragmatic epistemology and are committed to the idea of a pluralistic social science even if its results are time‐bound and culturally contingent. Naturalistic constructivists, such as Alexander Wendt (1999) and David Dessler (1999 ; Dessler and Owen 2005 ), aspire to make valid inferences on the causal mechanisms that underlie social life, and see much in common between the epistemologies of the social and natural sciences. These two groups' approach to methods are largely consistent with those of third‐generation qualitative researchers. 4 For instance, Peter Katzenstein's neoclassical constructivism (2005, x–xi, 40) is open to an analytically eclectic combination of interpretative and other methods. In contrast, Ruggie's final category of postmodernist constructivists take a much more skeptical view. This group, which grounds its works in the writings of Friedrich Nietzsche, Michel Foucault, and Jacques Derrida, focuses on the linguistic construction of social reality. They “make a decisive epistemic break with the precepts and practices of modernism” ( Ruggie 1998, 881 ) and hence are pessimistic about the prospects for a legitimate social science or for justifiable causal inferences. 5

Third‐generation qualitative methods have the potential to occupy a pivotal position in the discourses among different qualitative approaches. In some respects they can be seen as providing a unique bridge between the single‐logic‐of‐inference and interpretivist communities. Accepting comparison and intuitive regression as part of its underlying justification, the third‐generation case study approach is readily compatible with large‐n studies, as well as being accepting of many of the claims of the comparative advantages offered by quantitative methods. On the other hand, its close attention to detail, narrative, and context gives the third generation a close compatibility with interpretative approaches, especially with the pragmatist and naturalist branches. For the rest of the chapter, we consider some of the ways in which the third generation has developed and suggest potentially fruitful directions for future research. We focus on some key innovations in third‐generation qualitative methods over the last decade regarding within‐case analysis, comparative case studies, case selection, concepts and measurement, counterfactual analysis, typological theorizing, and Fuzzy Set analysis. We conclude with a discussion of promising avenues for future developments in qualitative methods, including ways of combining qualitative methods with statistical and/or formal methods, means of assessing theories involving various forms of complexity, ways of adapting qualitative methods to address common inferential biases uncovered by cognitive research, means of increasing the replicability and accessibility of qualitative data and using qualitative knowledge to improve codings in statistical databases, and ways of generalizing from case studies.

1 Innovations in Third‐Generation Qualitative Methods

Third‐generation qualitative scholars have over the last fifteen years revised or added to essentially every aspect of traditional case study research methods. 6 Although these new and updated methods vary along more than one dimension, Figure 29.1 presents them along a spectrum from methods of within‐case analysis of single cases at the left end, through methods of implicit comparison and small‐n comparison in the middle, to multi‐case comparisons and Fuzzy Set/Qualitative Comparative Analysis (FS/QCA) on the right. We very briefly discuss each of these methods in turn below.

Qualitative methods by numbers of cases and modes of analysis

1.1 Process Tracing

Methods of within‐case analysis have a long pedigree, but scholars have recently clarified their procedures and illuminated their foundations in the contemporary philosophy of science. A central method of within‐case analysis, termed process tracing ( George 1979 ; George and Bennett 2005 ), involves the close examination of the observable implications of alternative hypothesized explanations for a historical case. 7 The researcher using process tracing continually asks “if this explanation is accurate in this case, what else must be true about the processes through which the hypothesized causal mechanisms unfolded in this case?” The investigator then tests these hypothesized intervening variables against evidence from the case. With its emphasis on testing hypothesized causal mechanisms, often at a lower or more detailed level of analysis than the independent and dependent variables, process tracing is consistent with the “scientific realism” school of thought in the philosophy of science ( George and Bennett 2005 ).

Process tracing can involve both inductive analysis to generate hypotheses about a case and deductive tests of potential explanations of a case. A hypothesis can even be developed in a case and tested in that same case if it is tested against evidence that is in some way independent of the evidence that gave rise to it. In these regards, process tracing is closely analogous to traditional historical methods, as well as methods of developing and testing explanations of individual cases in epidemiology, pathology, geology, evolutionary biology, and detective work. Good process tracing requires giving attention to alternative hypotheses and their observable implications, taking into account potential biases in the available evidence, incorporating diverse sources of information, and providing as continuous as possible an explanation of the key sequential steps in a hypothesized process.

The logic of process tracing is quite similar to that of Bayesian inference ( Bennett 2007 ). 8 Like Bayesian inference, process tracing uses evidence to affirm some explanations and to cast into doubt, through eliminative induction, explanations that do not fit the evidence. Although there can be a problem of indeterminacy if there is no accessible evidence to discriminate between two competing and incompatible explanations of a case, it is also possible for one or a few pieces of evidence strongly to increase confidence in one explanation while also calling into question many others. Hence, in contrast to the single‐logic‐of‐inference approach to qualitative methods, the third‐generation view is that, to the extent that case studies rely on within‐case methods, they are not necessarily vulnerable to the “degrees of freedom critique.” This is because within‐case methods provide evidence that bears on multiple testable implications of alternative theories within a single case ( George and Bennett 2005, 28–9 ; see also Campbell 1975 ). Although case studies (and indeed all methods) are vulnerable to the more general problem of underdetermination of theories by evidence, the presence and severity of this problem in any particular case study research design depends, not on the number of variables or cases, but on whether the evidence from the cases is suitable for discriminating between alternative explanations. There is thus no inherent “degrees of freedom” problem in using process tracing to test several potential explanations in a single case.

1.2 Implicit Comparisons: Deviant, Most‐likely, Least‐likely, Crucial, and Counterfactual Cases

Just as language and concepts are inherently comparative, all single case studies, even when not explicitly comparative, are implicitly so. Case studies that are at least implicitly comparative include deviant, most‐likely, least‐likely, crucial, and counterfactual cases. Methodologists have clarified the uses of each of these kinds of case study in the last decade. Deviant cases are cases whose outcomes either do not conform to theoretical expectations or do not fit the empirical patterns observed in a population of cases of which the deviant case is considered to be a member. Prior statistical work can be useful in identifying deviant cases through these cases' high error term ( Seawright and Gerring 2006 ). Deviant cases are often useful for generating new hypotheses through inductive process tracing ( Eckstein 1975 ; George and Bennett 2005 ). A hypothesis generated from a deviant case may prove to be applicable to that case only, or to broad populations of cases. It is impossible to predict how generalizable the explanation for a deviant case might be until one has studied the case, developed an explanation, and considered the conditions under which the newly hypothesized underlying mechanisms might apply.

Most‐likely cases are those in which a theory is likely to provide a good explanation if it applies to any cases at all, and least‐likely cases are “tough test” cases in which the theory in question is unlikely to provide a good explanation. A theory that fails to fit a most‐likely case is strongly impugned, while a theory that fits even a case in which it is least likely gains confidence. A “crucial” case is a tough test in both senses: It must fit one explanation if the explanation is true, and it must not fit any other explanations. Harry Eckstein (1975) developed the ideas of crucial, most‐likely, and least‐likely cases in the 1970s, but scholars have more recently clarified that whether a case is most likely or least likely for a theory should be judged, not just by its values on the variables of that theory, but on the values of variables pointed to by alternative theories as well ( George and Bennett 2005 ; see also Gerring 2007 b ).

Counterfactual analysis is another form of implicit comparison, one in which the researcher compares an extant case with a counterfactual case that differs in one or more key respects. Philip Tetlock and Aaron Belkin have devised a number of standards for judging counterfactuals, including the “miminal re‐write rule” (changing as few variables as possible to construct the counterfactual) and the prescription that to the extent possible counterfactuals should include projectible and testable implications for the real world ( Tetlock and Belkin 1996 ). Counterfac‐ tual reasoning also serves as a useful test of consistency in a researcher's thinking, as every causal or explanatory claim about the world has a logically equivalent counterfactual claim. If a researcher finds a causal claim convincing, but does not find the logically equivalent counterfactual claim equally convincing, the researcher needs to consider whether there are asymmetries or faults in their theorizing about a case ( Lebow 2000 ; George and Bennett 2005 ; for applications to international relations cases, see Goertz and Levy 2007 ).

1.3 Small‐n Comparisons: Most‐similar and Least‐similar Cases, Comparative Historical Analysis

Methodologists have updated two forms of pairwise comparison that have a long pedigree: most‐similar and least‐similar case comparisons. In a most‐similar case comparison, two cases are similar in all but one independent variable, and differ on the outcome variable. In a least‐similar case comparison, two cases are similar on only one independent variable and have the same value on the dependent variable. These comparisons draw, respectively, on John Stuart Mill's “method of difference” and “method of agreement” (the seeming confusion of the terms arises from the fact Mill named his methods for the cases' difference or agreement on the dependent variable, while the contemporary labels correspond to similarity or lack thereof on the independent variables).

As Mill himself noted, inferences from these kinds of comparisons are potentially flawed for a variety of reasons: cases rarely differ in only one or all but one variable, there may be alternative paths to the same outcome (equifinality), some variables may be left out from the comparison, or there may be measurement error. More recently, methodologists have reaffirmed these potential threats to inferences in pairwise comparisons, but at the same time they have emphasized that process tracing helps reduce the likelihood of these problems ( George and Bennett 2005 ). In a most‐similar cases design, for example, process tracing can supplement the comparative analysis by using within‐case analysis to test whether the independent variable that differs between the two cases is related to the outcome through the hypothesized processes. Researchers can also use process tracing in this design to test whether other residual differences in the two cases' independent variables are related to the differences in the cases' outcomes.

Several of the innovations discussed in this chapter have been developed and/or deployed in the comparative historical analysis tradition, which straddles the disciplines of both political science and sociology. It has a particularly strong following in the subfields of comparative politics and American political development. James Mahoney and Dietrich Rueschemeyer (2003, 6) suggest that the comparative historical analysis approach addresses substantially important outcomes, and is defined by “a concern with causal analysis, an emphasis on process over time, and the use of systematic and contextualized comparison.” Uncomfortable with either universal generalizations or idiographic explanations, comparative historical analysis typically focuses on configurational analysis and on making contingent generalizations.

1.4 Typological Theorizing

Typological theorizing often involves a number of cross‐case comparisons within a single research design. Such theorizing uses a combination of these comparisons and within‐case analysis to develop theories about different configurations of variables and the outcomes to which they lead. One of the distinctive features of such theories is that they treat cases inherently as configurations of variables ( Ragin 1987 ; George and Bennett 2005 ), thereby allowing for the possibility of different multivariate interaction effects within each configuration.

Depending on the state of development of theories on the phenomenon of interest, the development of a typological theory can begin with established theories or it can proceed more inductively from individual case studies. In either event, the researcher usually iterates between evidence from cases and development of the theoretical framework, seeking with each iteration to uncover “new facts” to guard against the dangers of post hoc anomaly‐solving ( Lakatos 1970 ; Elman and Elman 2002 ). To build a typological theory, the investigator begins with the variables earlier research has identified (if any) on the phenomenon of interest. Using categorical measures of these variables, often dichotomous or trichotomous ones, the researcher outlines the “typological space” (termed a “truth table” in philosophy) of all the possible combinations of the variables. If, for example, there are four dichotomous independent variables and a dichotomous dependent variable, there will be two to the power of five or thirty‐two potential combinations or types.

Next, the researcher arrays the cases from the relevant population into the types that they best fit based on preliminary knowledge of the values of the variables in each case. This process can contribute to changes to the theoretical framework. If there are cases classified in the same type that the researcher thinks of as being dissimilar cases in important respects, for example, this can stimulate further consideration of the differences between the cases, and of any associated variables that might need to be added to the typological space to separate the cases into different types. Similarly, if cases with the same combination of independent variables have different outcomes, this poses a potential anomaly that merits attention.

Even after the apparent anomalies have been resolved to the extent possible using preliminary knowledge about the cases, at this point the typological space can be complex and seemingly unwieldy. Fortunately, there are several ways to reduce the typological space ( Elman 2005 ). First, the variables can be rescaled to a less detailed level of measurement if fine‐grained distinctions are not essential to the theory. Secondly, variables might be indexed, or aggregated into composite variables. Thirdly, the researcher can use logical compression to eliminate any empirically empty cells that are theoretically unlikely ever to include actual cases. A fourth option is empirical compression, eliminating empty cells whether or not they seem unlikely. A fifth route is pragmatic compression of adjacent types when their division serves no theoretical purpose. A sixth is to set aside from further and more detailed analysis types of cases whose outcomes appear to be theoretically overdetermined and whose empirical examples do not deviate from the expected outcomes. Finally, the researcher can decide to focus on a more narrowly circumscribed set of specific cells or subtypes of the phenomenon of interest.

Alternatively, if the typological space appears to be oversimplified, a researcher can use expansion (sometimes called “substruction”) to add variables and/or more finely grained distinctions back into the theory. Once the typological space has been reduced or expanded to the desired degree, it can contribute directly to the selection of cases that serve alternative research designs. Cases in adjacent cells that differ in one independent variable and in the dependent variable, for example, can be used for most‐similar comparisons, and cases with different outcomes from those of the other cases within the same cell constitute deviant cases that might be examined to try to identify left‐out variables. Examples of typological theories include those on burden‐sharing in ad hoc security coalitions ( Bennett, Lepgold, and Unger 1997 ), military occupations ( Edelstein 2008 ), status quo and revisionist regimes ( Schweller 1994 ; 1998 ), and types of federalist states ( Ziblatt 2006 ).

1.5 Fuzzy Set Analysis

Fuzzy Set (FS) analysis is another recent innovation in qualitative methods, one that typically includes studies of about ten to fifty cases ( Ragin and Rihoux 2004 ). A full explication of FS is beyond present purposes (see Ragin 2000 ), and we focus only on a few of its features and its comparative advantages vis‐à‐vis other qualitative methods.

FS methods are a variation on Charles Ragin's (1987) Qualitative Comparative Analysis (QCA), which uses “crisp” categorical variables and Boolean algebra to reduce populations of cases in truth tables to logical statements of necessity and sufficiency consistent with these cases. In contrast to crisp set QCA, FS methods assign “degree of membership” values between zero and one to cases based on the extent to which they are “fully in” a specified concept or collection of attributes. For example, a state that is “fully in” the conceptually defined set of “democracies” would be assigned a score of 1.0, a state whose attributes place it “mostly in” this set might have a score of 0.75, one that is “more in than out” might be a 0.5, and so on. After a researcher has assigned FS values to the cases in a study, he or she can use statistical tests to assess whether the outcomes of a particular type of case are consistent enough to sustain a claim of (near) necessity or sufficiency (in contrast to QCA, FS methods can use probabilistic statements).

FS analysis is a comparative method that does not necessarily rely on within‐case analysis of individual cases, although it does require sufficient information about each case to assign it an FS value, and it is not incompatible with within‐case analysis. FS analysis differs from typological theorizing in that it tends to assume that outlier cases can arise by chance, whereas typological theorizing typically uses a default assumption that deviant cases are potential sources for identifying left‐out variables. In addition, FS analysis is best suited to subjects for which prior theories are well established, for which diversity of cases is not sharply limited, and for the goal of testing claims of necessity or sufficiency rather than that of generating new theories ( Bennett and Elman 2006 a ). Typological theorizing, on the other hand, can be used both for testing and generating theories and for explaining individual cases.

1.6 Innovations in Conceptual Analysis, Two‐level Theories, and Case Selection

There are three other important sets of recent innovations in case study methods that do not fit neatly on the spectrum from single to multiple case study designs but are applicable to many of these designs. First, methodologists have clarified the role and procedures of developing and refining concepts. Robert Adcock and David Collier (2001) have outlined the relationships among background concepts, systematized concepts, indicators, and scores on individual cases, noting that there are often iterative changes from one level to another in the course of research. Collier and Stephen Levitsky (1997) have pointed out the prevalence and uses of “diminished subtypes,” or conceptual categories that lack one or more of the attributes of full examples of the phenomenon in question. Collier, Hidalgo, and Maciuceanu (2006) have unpacked and updated the debate over essentially contested concepts. John Gerring (2001 ; Gerring and Barresi 2003 ) has clarified the trade‐offs among different desiderata of qualitative concepts and measures, as well as suggested guidelines for concept formation. Gary Goertz (2006) has distinguished between necessary/sufficient concepts, or concepts for which some component is necessary or sufficient, and family resemblance concepts, for which membership in a conceptual category is determined by having a specified minimal level of several substitutable attributes.

Secondly, Goertz and Mahoney (2005) have identified “two‐level theories” as an important pattern of theorizing common to many qualitative studies. Two‐ level theories can combine elements of necessity at one level that interact with family resemblance relationships at another level. Goertz and Mahoney illustrate this with Theda Skocpol's famous theory (1979) on social revolutions, in which state breakdown and peasant revolt are both necessary for social revolution, but either of these conditions can be achieved through several different substitutable routes. There are many possible kinds of two‐level theories, which can be depicted either as a flowchart diagram or as a typological space (for an example showing the correspondence of these two forms of presentation, see Bennett 1999, 109–10 ).

Thirdly, several methodologists have clarified the problems of case selection and selection bias in case study research designs. Proponents of a single logic of inference level strong criticisms at qualitative methods undertaken without a proper appreciation for what they consider to be universally applicable quantitative rules of inference. One common critique is that case study research designs often involve the investigator selecting cases for study based on prior knowledge of these cases' outcomes. The most common form of this critique is that the selection of cases on the basis of values of the dependent variable leads to an underestimation of the effects of the independent variable ( King, Keohane, and Verba 1994 ; Geddes 2003, 87 ).

Third‐generation methodologists, however, argue that the challenge of case selection in qualitative research is often misunderstood when it is viewed through the prism of case selection biases in observational statistical studies. Properly understood, case selection procedures in qualitative research designs could in some instances be more damaging to causal inference than the standard statistical critique suggests, but often these procedures are in fact well adapted to the inferential purposes for which qualitative researchers use them. Selection on the dependent‐ variable and no‐variance designs have important uses in case study research. A single deviant case, for example, can prove fruitful in identifying a new variable, even though such a case is selected on the dependent variable. As noted above, although a deviant case is seemingly a “no‐variance” design, it is chosen for implicit or explicit comparison to a theoretical or empirical pattern from which it varies. Moreover, no‐variance single cases selected on the dependent variable can test claims of necessity or sufficiency ( Dion 1998 ).

In addition, the statistical selection bias critique assumes a preconstituted population, but if the researcher has no such population in mind and is trying to learn more about similarities among positive cases before identifying the relevant underlying population, selection on the dependent variable is justifiable. Otherwise, “addressing the question of selection bias before establishing an appropriate population puts the cart before the horse” ( Collier, Mahoney, and Seawright 2004, 88 ). In addition, the selection bias critique does not apply to process tracing in the same way that it does to cross‐case comparative methods, as process tracing does not rely on cross‐case covariation ( Collier and Mahoney 1996 ; Collier, Mahoney, and Seawright 2004, 96 ). As noted above, even comparative cases research designs, such as the most‐similar cases design, draw much of their inferential power from process tracing. In short, although variance on the independent and dependent variables is essential for many kinds of case study research designs and inferential goals, it is not necessary or even useful for all such designs and goals.

Another area of innovation regarding case selection concerns an important issue that researchers using both qualitative and quantitative methods often overlook: the problem of defining and selecting negative cases of a phenomenon, or contexts in which the outcome of interest could have happened but did not. The inclusion of irrelevant cases in a statistical study, such as including in a study of inter‐state wars dyads of far distant countries with no capability or motivation to fight one another, can make a theory look stronger than it actually is. Mahoney and Goertz (2004) have suggested a “possibility principle” for identifying relevant cases by a “rule of inclusion,” in which cases are included if the value of at least one independent variable points to the outcome of interest, and a “rule of exclusion,” through which cases are excluded if they have a variable at a value known through previous studies to make the outcome of interest impossible. These authors note that these rules are in part theory dependent and should not be applied mechanically, and in fact there may be many variants on these rules depending on the nature of prior knowledge of the phenomenon in question. Whatever criteria one chooses for identifying negative cases, the task of identifying them as rigorously as possible is important for many studies.

2 New Frontiers in Qualitative Methods

Innovations in qualitative methods are ongoing, and five areas in particular deserve mention as current or potential subjects for further development. First, the development of multi‐method research designs is already well under way, led by empirical research examples rather than by systematic analyses by methodologists of alternative ways of combining different methods. There are several excellent examples of international relations research designs combining case study methods with formal models, statistical analysis or both. Other methods, including experiments and ethnographic research, can be combined with case studies as well. The great advantage of combining methods is that each approach offers the potential for at least partly offsetting the limitations of another. The challenge of multi‐method research, particularly for graduate students, is that a great deal of time and skill are required to develop expertise in more than one method and to gather the evidence each method requires. Scholars have only just begun addressing the question of how to combine methods more generally ( Lieberman 2005 ; Seawright and Gerring 2006 ; Bennett and Elman 2007 b ), and much more work on this subject remains to be done.

Secondly, qualitative methodologists have begun focusing on how to assess theories that involve different forms of complexity. Several have investigated issues related to path dependence and ways in which qualitative methods can address them ( Mahoney 2000 ; Bennett and Elman 2006 b ). Goertz and Mahoney (2005) address a different form of complexity in their work on various combinations of necessity and family resemblance relations in two‐level theories. Fuzzy Set analysis and typological theorizing are ways of addressing the related challenge of multivariate interaction effects. There is potential for further advances in these and other areas of complexity theory, perhaps drawing on work from other sciences that have confronted the problems of complexity, such as evolutionary biology.

Thirdly, qualitative methods need to keep pace with developments in the cognitive sciences. One role for rigorous methodological procedures is to safeguard against our own cognitive biases. Many procedures in both qualitative and quantitative methods, for example, are geared to guard against the dangers of confirmation bias, which have been amply demonstrated in laboratory experiments. Research in cognitive psychology and behavioral economics has pointed to many other kinds of inferential biases ( Kahneman, Slovic, and Tversky 1982 ), and studies in political psychology have demonstrated that political scientists are vulnerable to such biases ( Tetlock 2005 ). Recent work suggests that a few simple procedures, such as asking individuals to think counterfactually about the conditions under which their predictions might be proved wrong, can improve performance at inferential tasks like Bayesian updating ( Herrmann and Choi 2007 ). Qualitative methodologists, and methodologists more generally, need to mine the large and growing literature on cognitive biases and systematically develop procedures for addressing them.

A fourth area for further development is that of improving the access to and replicability of qualitative evidence. Qualitative researchers can make much greater use of improved technologies for gathering and storing audio and visual data and making these data web‐accessible. Field notes, audio and videotapes of interviews and events, photographs of symbols and artifacts, and other kinds of qualitative data can be made accessible and linked to publications. As more such evidence becomes available online, research organizations like the National Science Foundation need to address the question of whether they can play a role in providing storage space for such information, and to consider whether existing open‐source search engines are adequate for the task of enabling users easily to find what they need. Methodologists and communities of scholars also need to devise standards and protocols on the presentation and replicability of such qualitative data. There is a role as well for qualitative researchers with regional and functional expertise to contribute to the improvement of quantitative databases, and cumulatively to apply their knowledge toward making the codings in such databases more accurate ( Bowman, Lehoucq, and Mahoney 2005 ). Web‐based means of soliciting and vetting community input, similar to the process used by Wikipedia, may prove helpful here.

Finally, qualitative methodologists need to renew their focus on the challenge of generalizing from individual and comparative case studies. The findings of studies of deviant cases, and of studies that affirm a theory in a least‐likely case or undermine it in a most‐likely case, may be widely generalizable, or they may prove to be limited only to the case studied. The standards for assessing the generalizability of findings from such cases need to be clarified, and researchers need to be more precise in stating whether they think their findings apply only to the case under study, to some type or category of configurative cases of which it is member, or to broad populations sharing only one or a few features of the case studied. Put another way, qualitative researchers need to clarify the conditions under which they can claim different kinds of scope conditions for their theories based on the cases they study ( Goertz and Mahoney 2006 ). These five tasks pose important and potentially fruitful challenges for the next generation of qualitative researchers and methodologists.

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In 2007, the institute name was changed from the Institute for Qualitative Research Methods.

International relations scholars may already be familiar with some of the third‐generation works centrally located in their subfield—e.g. Tetlock and Belkin (1996) ; George and Bennett (2005) — but the larger canon also includes works by Bates et al. (1998) ; Ragin (2000) ; Elman and Elman (2001 ; 2003 ); Gerring (2001 ; 2007 a ) ; Goertz and Starr (2002) ; Mahoney and Rueschemeyer (2003) ; Brady and Collier (2004) ; Pierson (2004) ; Goertz (2006) ; and Goertz and Levy (2007) .

See, e. g., the list of thirty‐five varieties of interpretative research methods listed in table 1 in the introduction to Yanow and Schwartz‐Shea (2006) .

There is a substantial overlap here with scholars whom Christian Reus‐Smit (2002, 495) identifies as “methodological conventionalists.” These two categories also roughly coincide with what Andrew Hurd (this volume) identifies as “positivists,” though in our view many international relations scholars, whether constructivist or not, are methodologically conventional but do not subscribe to traditional positivist notions of “laws” and “falsifiability.”

See also Yanow (2006, 6) , and Yanow and Schwartz‐Shea (2006, xxxvi , n. 15). Similarly, Friedrich Kratochwil (this volume) raises several hermeneutic critiques of the most ambitious form of scientific realism, specifically the claim that theories in some sense “refer” in progressively more accurate ways to underlying “realities.” Kratochwil does not go so far as suggesting, however, that there is no basis for judging some interpretations to be superior to others, and it is not clear whether he objects as well to more modest forms of scientific realism that do not presume science is always progressive (for an analysis of different varieties of scientific realism, see Chernoff 2002 ).

The discussion below draws upon and further develops our previous separate and joint writings on qualitative methods, including George and Bennett (2005) ; and Bennett and Elman (2006 a ; 2006 b ; 2007 a ; 2007 b ).

See also Collier, Brady, and Seawright's discussion (2004, 252–5) of causal process observations.

The Bayesian approach to theory testing focuses on updating degrees of belief in the truth of alternative explanations. In other words, Bayesians treat statistical parameters probabilistically, attaching subjective probabilities to the likelihood that hypotheses are true and then updating these probabilities in the light of new evidence. In contrast, frequentist statistics attaches probabilities to the likelihood of getting similar results from repeated sampling of a population. Bayesian inference can apply to one or a few cases or pieces of evidence, whereas frequentist statistical analysis needs a higher number of cases to allow inferences, although the two forms of inference should converge on similar results as the number of cases or pieces of evidence grows. Process tracing follows a logic that is very similar to Bayesian reasoning, updating degrees of belief in alternative explanations of a case in light of evidence generated from within that case. For further discussion of the similarities between process tracing and Bayesian inference, see Bennett (2007) .

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations, authors’ contributions.

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

Availability of data and materials

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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

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

• Prepare and organize 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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What is Qualitative Research? Methods and Examples

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What is Qualitative Research? Methods and Examples was originally published on Forage .

Qualitative research seeks to gain insights and understand people’s experiences and perspectives by studying social organizations and human behavior. Data in qualitative studies focuses on people’s beliefs and emotional responses. Qualitative data is especially helpful when a company wants to know how customers feel about a product or service, such as in user experience (UX) design or marketing . 

In this guide, we’ll go over:

Qualitative Research Definition

Qualitative research methods and examples, advantages and disadvantages of qualitative approaches, qualitative vs. quantitative research, showing qualitative research skills on resumes.

Researchers use qualitative approaches to “determine answers to research questions on human behavior and the cultural values that drive our thinking and behavior,” says Margaret J. King, director at The Center for Cultural Studies & Analysis in Philadelphia.

Data in qualitative research typically can’t be assessed mathematically — the data is not sets of numbers or quantifiable information. Rather, it’s collections of images, words, notes on behaviors, descriptions of emotions, and historical context. Data is collected through observations, interviews, surveys, focus groups, and secondary research. 

However, a qualitative study needs a “clear research question at its base,” notes King, and the research needs to be “observed, categorized, compared, and evaluated (along a scale or by a typology chart) by reference to a baseline in order to determine an outcome with value as new and reliable information.”

Who Uses Qualitative Research?

Researchers in social sciences and humanities often use qualitative research methods, especially in specific areas of study like anthropology, history, education, and sociology. 

Qualitative methods are also applicable in business, technology , and marketing spaces. For example, product managers use qualitative research to understand how target audiences respond to their products. They may use focus groups to gain insights from potential customers on product prototypes and improvements or surveys from existing customers to understand what changes users want to see. 

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Grounded Theory

Grounded theory is an inductive approach to theory development. In many forms of research, you begin with a hypothesis and then test it to see if you’re correct. In grounded theory, though, you go in without any assumptions and rely on the data you collect to form theories. You start with an open question about a phenomenon you are studying and collect and analyze data until you can form a fully-fledged theory from the information. 

Example: A company wants to improve its brand and marketing strategies. The company performs a grounded theory approach to solving this problem by conducting interviews and surveys with past, current, and prospective customers. The information gathered from these methods helps the company understand what type of branding and marketing their customer-base likes and dislikes, allowing the team to inductively craft a new brand and marketing strategy from the data. 

Action Research

Action research is one part study and one part problem-solving. Through action research, analysts investigate a problem or weakness and develop practical solutions. The process of action research is cyclical —- researchers assess solutions for efficiency and effectiveness and create further solutions to correct any issues found. 

Example: A manager notices her employees struggle to cooperate on group projects. She carefully reviews how team members interact with each other and asks them all to respond to a survey about communication. Through the survey and study, she finds that guidelines for group projects are unclear. After changing the guidelines, she reviews her team again to see if there are any changes to their behavior.  

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Phenomenological Research

Phenomenological research investigates a phenomenon in depth, looking at people’s experiences and understanding of the situation. This sort of study is primarily descriptive and seeks to broaden understanding around a specific incident and the people involved. Researchers in phenomenological studies must be careful to set aside any biases or assumptions because the information used should be entirely from the subjects themselves. 

Example : A researcher wants to better understand the lived experience of college students with jobs. The purpose of this research is to gain insights into the pressures of college students who balance studying and working at the same time. The researcher conducts a series of interviews with several college students, learning about their past and current situations. Through the first few interviews, the researcher builds a relationship with the students. Later discussions are more targeted, with questions prompting the students to discuss their emotions surrounding both work and school and the difficulties and benefits arising from their situation. The researcher then analyzes these interviews, and identifies shared themes to contextualize the experiences of the students. 

Ethnography

Ethnography is an immersive study of a particular culture or community. Through ethnographic research, analysts aim to learn about a group’s conventions, social dynamics, and cultural norms. Some researchers use active observation methods, finding ways to integrate themselves into the culture as much as possible. Others use passive observation, watching closely from the outside but not fully immersing themselves. 

Example: A company hires an external researcher to learn what their company’s culture is actually like. The researcher studies the social dynamics of the employees and may even look at how these employees interact with clients and with each other outside of the office. The goal is to deliver a comprehensive report of the company’s culture and the social dynamics of its employees. 

Case Studies

A case study is a type of in-depth analysis of a situation. Case studies can focus on an organization, belief system, event, person, or action. The goal of a case study is to understand the phenomenon and put it in a real-world context. Case studies are also commonly used in marketing and sales to highlight the benefits of a company’s products or services. 

Example: A business performs a case study of its competitors’ strategies. This case study aims to show why the company should adopt a specific business strategy. The study looks at each competitor’s business structure, marketing campaigns, product offerings, and historical growth trends. Then, using this data on other businesses, the researcher can theorize how that strategy would benefit their company. 

>>MORE: Learn how companies use case study interviews to assess candidates’ research and problem-solving skills. 

Qualitative research methods are great for generating new ideas. The exploratory nature of qualitative research means uncovering unexpected information, which often leads to new theories and further research topics. Additionally, qualitative findings feel meaningful. These studies focus on people, emotions, and societies and may feel closer to their communities than quantitative research that relies on more mathematical and logical data. 

However, qualitative research can be unreliable at times. It’s difficult to replicate qualitative studies since people’s opinions and emotions can change quickly. For example, a focus group has a lot of variables that can affect the outcome, and that same group, asked the same questions a year later, may have entirely different responses. The data collection can also be difficult and time-consuming with qualitative research. Ultimately, interviewing people, reviewing surveys, and understanding and explaining human emotions can be incredibly complex. 

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While qualitative research deals with data that isn’t easily manipulated by mathematics, quantitative research almost exclusively involves numbers and numerical data. Quantitative studies aim to find concrete details, like units of time, percentages, or statistics. 

Besides the types of data used, a core difference between quantitative and qualitative research is the idea of control and replication. 

“Qualitative is less subject to control (as in lab studies) and, therefore, less statistically measurable than quantitative approaches,” says King.

One person’s interview about a specific topic can have completely different responses than every other person’s interview since there are so many variables in qualitative research. On the other hand, quantitative studies can often be replicated. For instance, when testing the effects of a new medication, quantifiable data, like blood test results, can be repeated. Qualitative data, though, like how people feel about the medication, may differ from person to person and from moment to moment. 

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You can show your experience with qualitative research on your resume in your skills or work experience sections and your cover letter . 

In your skills section, you can list types of qualitative research you are skilled at, like conducting interviews, performing grounded theory research, or crafting case studies. 

You can highlight specific examples in the description of your past work or internship experiences. For example, you can talk about a time you used action research to solve a complex issue at your last job. 

Your cover letter is an excellent place to discuss in-depth qualitative research projects you’ve completed. For instance, say you spent a summer conducting ethnographic research or a whole semester running focus groups to get feedback on a product. You can talk about these experiences in your cover letter and note how these skills make you a great fit for the job. 

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