Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques

Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques

Edited by: Benoît Rihoux & Charles C. Ragin Published: 2009
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  • Applied Social Research Methods Series

    Series Editors

    LEONARD BICKMAN Peabody College, Vanderbilt University, Nashville

    DEBRA J. ROG, Westat

      • by FLOYD J. FOWLER, Jr.
      • by HARRIS COOPER
      • by ANN MAJCHRZAK
    • SECONDARY RESEARCH (Second Edition)
    • CASE STUDY RESEARCH (Second Edition)
      • by ROBERT K. YIN
      • by PAUL J. LAVRAKAS
      • by CARL M. MOORE
      • by JACK McKILLIP
      • by ALLAN J. KIMMEL
      • by NORMAN K. DENZIN
    • ETHNOGRAPHY (Second Edition)
    • FOCUS GROUPS (Second Edition)
      • by GART T. HENRY
      • by ARNOLD J. LOVE
      • by ROBERT F. DeVELLIS
      • by JOAN E. SIEBER
      • by ROBERT K. YIN
      • by GARY T. HENRY
      • by DONNA M. MERTENS and John A. McLaughlin
      • by FLOYD J. FOWLER, Jr.
      • by JOSEPH A. MAXWELL
      • by BRIAN T. YATES
      • by ROBERT F. BORUCH
  • Copyright
  • List of Boxes, Figures, and Tables


    Box 0.1 About Terminology and Labels: QCA, csQCA, mvQCA, fsQCA, and Software xix

    Box 0.2 Goals of This Textbook xx

    Box 0.3 Pedagogical Resources in This Textbook xxiii

    Box 1.1 “Multiple Conjunctural Causation” in a Nutshell 8

    Box 1.2 Causal Relations in QCA: Assumptions That Are Not Taken Onboard 9

    Box 1.3 Necessity and Sufficiency Back in the Picture 10

    Box 1.4 Five Types of Uses of QCA Techniques 15

    Box 2.1 An Empirical Example Used Throughout This Textbook: The “Inter-war Project” 21

    Box 2.2 “Good Practices” (1): Case Selection in Small- and Intermediate-N Research 24

    Box 2.3 “Good Practices” (2): Condition Selection in Small- and Intermediate-N Research Designs 28

    Box 2.4 Main Steps of the MSDO/MDSO Procedure 29

    Box 3.1 Main Conventions and Operations of Boolean Algebra 34

    Box 3.2 What Is Boolean Minimization? 35

    Box 3.3 “Good Practices” (3): How to Dichotomize Conditions in a Meaningful Way 42

    Box 3.4 Five Types of Configurations 44

    Box 3.5 “Good Practices” (4): Things to Check to Assess the Quality of a Truth Table 45

    Box 3.6 “Good Practices” (5): How to Resolve Contradictory Configurations 48

    Box 3.7 “Good Practices” (6): Four Complete Minimization Procedures to Be Run and Made Explicit 64

    Box 4.1 mvQCA Notation: Main Conventions 73

    Box 4.2 The mvQCA Minimization Rule 74

    Box 4.3 Inclusion of Logical Remainders in mvQCA Minimization 76

    Box 4.4 “Good Practices” (7): Threshold-Setting With mvQCA 78

    Box 4.5 “Good Practices” (8): Specific to mvQCA 84

    Box 5.1 Fuzzy-Set Membership Scores: What Are They? 90

    Box 5.2 “Good Practices” (9): Specific to the Calibration of Fuzzy Sets 93

    Box 5.3 The Main Operations on Fuzzy Sets 99

    Box 5.4 The Criterion of Consistency in Fuzzy Sets, in a Nutshell 108

    Box 5.5 “Good Practices” (10): Specific to fsQCA 118

    Box 6.1 Six Approaches to Selection of Conditions in QCA 125

    Box 6.2 “Good Practices” (11): Technical Arbitrations and Practical Steps Throughout the QCA Procedures 138

    Box 7.1 The Importance of Case-Based Knowledge to Open Up the “Black Box” of Causal Processes 160

    Box 8.1 “Good Practices” (12): Transparency 168

    Box G.1 Good Practices” (13): Words Matter, So Use the Correct Terminology! 181


    Figure 1.1 Comparative Analysis: A Typology 5

    Figure 2.1 Most Different and Most Similar Systems Designs 23

    Figure 3.1 Venn Diagram Corresponding to Table 3.1 (3 Condition Variables) 38

    Figure 3.2 Venn Diagram Corresponding to Table 3.4 (4 Conditions) 47

    Figure 3.3 Venn Diagram (5 Conditions; GNPCAP Recoded) 56

    Figure 3.4 Venn Diagram: Solution for the [1] Outcome (With Logical Remainders) 62

    Figure 3.5 Venn Diagram: Solution for the [0] Outcome (With Logical Remainders) 63

    Figure 4.1 Data Distribution Not Allowing a Simple Dichotomization 71

    Figure 4.2 Using Two Thresholds for the GNPCAP Condition 79

    Figure 4.3 Venn Diagrams Adapted for a Multi-Value Data Set 82

    Figure 5.1 Plot of Degree of Membership in BREAKDOWN Against Degree of Membership in the ∼D * ∼U * ∼L Combination (Corner of Vector Space) 103


    Table 3.1 Raw Data Table (3-Condition Example) 37

    Table 3.2 Lipset's Indicators, Raw Data (4 Conditions) 41

    Table 3.3 Lipset's Indicators, Dichotomized Data (4 Conditions) 43

    Table 3.4 Truth Table of the Boolean Configurations 45

    Table 3.5 Lipset's Indicators, Raw Data, Plus a Fifth Condition 51

    Table 3.6 Lipset's Indicators, Dichotomized Data, Plus a Fifth Condition 52

    Table 3.7 Truth Table of the Boolean Configurations (4 + 1 Conditions) 53

    Table 3.8 Lipset's Indicators, Dichotomized Data, Plus a Fifth Condition (and GNPCAP Recoded) 54

    Table 3.9 Truth Table of the Boolean Configurations (4 + 1 Conditions, GNPCAP Recoded) 55

    Table 4.1 Dichotomous Coding of a 3-Value Traffic Light 71

    Table 4.2 Multi-Valued Scale Classifying Children's Age 74

    Table 4.3 Lipset's Indicators, Multi-Value Truth Table (4 Conditions) 80

    Table 5.1 Crisp Versus Fuzzy Sets 91

    Table 5.2 Data Matrix Showing Original Variables and Fuzzy-Set Membership Scores 95

    Table 5.3 Illustration of Logical AND (Intersection) 97

    Table 5.4 Illustration of Logical OR (Union) 98

    Table 5.5 Cross-Tabulation of Outcome Against Presence/Absence of a Causal Combination 100

    Table 5.6 Fuzzy-Set Membership of Cases in Causal Combinations 101

    Table 5.7 Correspondence Between Truth Table Rows and Vector Space Corners 105

    Table 5.8 Distribution of Cases Across Causal Combinations and Set-Theoretic Consistency of Causal Combinations as Subsets of SURVIVED 113

    Table 5.9 Distribution of Cases Across Causal Combinations and Set-Theoretic Consistency of Causal Combinations as Subsets of BREAKDOWN 116

  • Acknowledgments

    This textbook is the result of a collective endeavor, not only because it involves seven contributors but also because it has been made possible by the support of numerous colleagues, collaborators, and students around the globe.

    We are indebted to the late Kriss Drass for software development. Kriss was responsible for implementation of all versions of QCA-DOS through 3.0 and for all versions of FSQCA through 0.9. Sean Davey has taken on responsibility for subsequent versions of FSQCA (1.0 through 3.0), including implementation of the fuzzy-set truth table procedure and the various procedures for counterfactual analysis.

    We thank Nancy Martin for translation work during the early stages. We also wish to mention some colleagues who have played active roles in setting up activities and further developing the COMPASSS network, which has provided opportunities to develop new ideas and collaborative projects: Peter Bursens, Heike Grimm, David Levi-Faur, Axel Marx, Daishiro Nomiya, Wendy Olsen, Fritz Sager, and Geert Van Hootegem. We also thank close colleagues and friends with whom we have debated at length over comparative methods, among them Frank Aarebrot, David Collier, Andrù-Paul Frognier, Gary Goertz, Airo Hino, Bruce Kogut, Lars Mjøset, and Marc Swyngedouw.

    We also wish to thank Academia-Bruylant for its support in the publication of a first, French-language, textbook on csQCA (De Meur & Rihoux, 2002), which paved the way for this first comprehensive English-language textbook, Configurational Comparative Methods. The completion of this volume was also made possible by the support of the Fonds de la Recherche Fondamentale Collective (FRFC), through the Fonds National de la Recherche Scientifique (FNRS, Belgium), with the research grant on “Analyse de l'ùmergence des nouvelles institutions à parties prenantes multiples (multi-stakeholder) pour la rùgulation politique et sociale des conditions de travail et de la protection de l'environnement dans des marchùs globaux” (ref. 2.4.563.05.F). Work was also facilitated by a grant from the European Science Foundation (ESF), to organize an exploratory workshop on “Innovative comparative methods for policy analysis: An interdisciplinary European endeavour for methodological advances and improved policy analysis/evaluation,” held in Erfurt from September 25–28, 2004 (ref. EW03–217). The fine working atmosphere and bibliographical resources at the Centre de Politique Comparùe (Université catholique de Louvain) also helped us a great deal in finalizing the manuscript.

    We are thankful to numerous colleagues who have given us feedback in the course of the development of the FSQCA and TOSMANA software, including

    Helen Giesel, Gary Goertz, Bruce Kogut, Jon Kvist, Carsten Schneider, Steve Vaisey, and Claudius Wagemann. More specifically, we received most useful feedback on previous versions of this textbook from Simone Ledermann, Raphaela Schlicht, Carsten Schneider, Svend-Erik Skaaning, and Claudius Wagemann, as well as from two anonymous reviewers. We also received specific comments from Olaf Van Vliet and Maarten Vink. Finally, we owe special thanks to Svend-Erik Skaaning, who has replicated the QCA analyses with some of his students at the University of Aarhus while we were finalizing this textbook—this has been most helpful in the technical consolidation of Chapters 3 through 5.

    The list of colleagues, researchers and students who have given us feedback on “bits and pieces” of this textbook in conferences, seminars, and courses is naturally too long to mention, but their comments have also been valuable. In particular, we thank participants to courses we taught at the ECPR Summer School in Methods and Techniques (Ljubljana), the Essex Summer School in Social Science Data Analysis and Collection (Colchester), the Oslo Summer School in Comparative Social Science Studies, the Institute for Qualitative and Multi-Method Research (Phoenix), and the Ecole d'ùtù en mùthodes quantitatives en sciences sociales (Lille). Naturally, the organizers of these venues are also to be thanked—nothing replaces such training venues when it comes to testing teaching material and new ideas.

    Last but not least, we thank the Sage team for their diligent support and efficiency, in particular Lisa Cuevas for enabling the ship to set its sails, Vicki Knight for ensuring safe arrival at the harbor after a long voyage, Sean Connelly for overall satellite guidance, Sarah Quesenberry for skillfully operating the dry dock facilities, Tony Moore and Dorothy Hoffman for polishing every square inch and checking every bolt from bow to stern, and finally Lauren Habib and Stephanie Adams for spreading the word around the globe, so that crowds will come visit this first vessel of its kind as it embarks on the conquest of the seven seas. On the practical side, in the team of contributors to this book, Sakura Yamasaki and Damien Bol have played a crucial role in the maintenance of the COMPASSS bibliographical database out of which the list of references has been extracted. All this being said, we fiercely claim intellectual property on any remaining error, approximation, or omission.

  • Introduction
    BenoîtRihouxCharles C.Ragin
    Why Compare? Why Configurational Comparative Methods?

    Comparison lies at the heart of human reasoning and is always there in the observation of the world—“thinking without comparison is unthinkable” (Swanson, 1971, p. 45). Indeed, even the observation of singular phenomena is empty if we do not engage in a comparison: A phenomenon or object can be identified as such only if it is recognized as different from other phenomena or objects (Aarebrot & Bakka, 2003). For instance, we know that apples are not pears because we have compared the two.

    More specifically, comparison is a key operation in any empirical scientific effort. There is a long line of scholars who have reflected upon this—and applied this empirically—all the way from Aristotle (probably the founder of a rigorous comparative approach) to de Tocqueville, Weber and Durkheim, and on to more contemporary works by Sartori (e.g., 1970, 1991), Lijphart (e.g., 1971) and Marradi (1985). For one thing, any descriptive effort, any typology or classification involves comparison (Bailey, 1994). To consider both apples and pears as belonging to the category of “fruits,” we must compare “fruits” and “non-fruits” in the broader category of “plants,” and so on. Once we have defined the category of “fruits,” we can come to the conclusion that an orange is also a fruit, by comparing some key properties of an orange with those of an apple and of a pear. While also being a fruit, an orange shares some specific characteristics with lemons and grapefruits. Thus oranges and lemons, on the one hand, and apples and pears, on the other hand, belong to two different subtypes of fruits.

    Of course, such everyday comparisons may seem trivial, and indeed many of those mental operations remain implicit in our reasoning. The purpose of this textbook is to demonstrate that comparison, as a basic and powerful mental operation, can be translated into a set of systematic comparative methods and techniques. Although this volume is not about fruits, we shall demonstrate that such methods and techniques can indeed be fruitfully applied in many disciplines, in the social sciences broadly defined, and also beyond.

    Systematic comparison is a key operation in all experimental and natural sciences. For instance, we know that water is boiling when it is heated to 100°C because we have compared the state of water below 100°C, at 100°C, and above 100°C, while controlling for contextual parameters such as atmospheric pressure and altitude above sea level. Incidentally, because we are able to control all these contextual parameters, and because we are able to manipulate one specific condition—temperature—we are able to demonstrate that a change in temperature actually causes the water to boil. This is why the experimental sciences are able to make such strong and simple causal statements.

    Yet, in most social and behavioral sciences, perhaps with the exception of some branches of psychology, real-life laboratory-like experimentation is neither empirically possible nor ethically desirable. To pursue our example further: In antiquity, slaves were used to scientifically demonstrate that water that is brought to a boiling temperature also happens to burn human skin, by comparing the effect of plunging the hand of a slave into lukewarm, as compared to boiling, water. And Cleopatra used slaves to examine the more or less lethal effects of various poisons in her apples and oranges. Obviously, contemporary social scientists cannot do this, nor would they want to.

    This is where the comparative method comes into play. It can be considered a crude substitute for experimentation (Lijphart, 1971): We observe empirical phenomena—analytical units, “cases” (Ragin & Becker, 1992)—while also controlling for contextual conditions (see Chapter 1). In social science, those cases, as we know, are intrinsically complex, multifaceted, often with blurred boundaries. This is why “thick,” single-case studies have always played an important role in many disciplines: They allow a deep understanding of that single case. The main limitation of single-case studies, however, is that it is very difficult to engage in any form of generalization, as the key findings and conclusions are mostly limited to that single case.

    How can one compare these complex cases? During recent decades, an increasing number of social scientists have been opting for multiple case studies as a research strategy. This strategy aims at meeting the need to gather indepth insight into different cases and to capture their complexity, while still attempting to produce some form of generalization (Ragin, 1987). It also coincides, during the last few years, with renewed interest in case-oriented research (e.g., George & Bennett, 2005; Gerring, 2006; Mahoney & Rueschemeyer, 2003). Such a strategy is also adopted because many relevant and interesting objects are “naturally” limited in number: nation states or regions, political crises, wars, firms of a certain type, and so on. These are naturally limited, or “small-N” (or “intermediate-N”—see Chapter 2), populations of cases.

    In many instances the (ex post) comparison of case study material is rather “loose” or not formalized. The major ambition of the methods and techniques presented in this textbook is to allow systematic cross-case comparisons, while at the same time giving justice to within-case complexity, particularly in small- and intermediate-N research designs.

    The cover heading for all these methods and techniques is Configurational Comparative Methods (CCM). In a nutshell, this heading indicates that in order to enable the systematic comparative analysis of complex cases, those cases must be transformed into configurations. Simply said, a configuration is a specific combination of factors (or stimuli, causal variables, ingredients, determinants, etc.—we call these conditions in CCM terminology) that produces a given outcome of interest. As shall be explained at length in the next chapters, the conditions will be envisaged in a combinatorial way—hence enabling one to model quite a high level of complexity even with only a few conditions.

    One key question we shall address is the following: Which conditions (or combinations thereof) are “necessary” or “sufficient” (or possibly both necessary and sufficient) to produce the outcome? In a non-formal way (for more on this, see p. 10, Box 1.3; see also Caramani, 2008), let us say at this stage that:

    • A condition is necessary for an outcome if it is always present when the outcome occurs. In other words, the outcome cannot occur in the absence of the condition.
    • A condition is sufficient for an outcome if the outcome always occurs when the condition is present. However, the outcome could also result from other conditions.

    For instance, holding competitive elections is a necessary condition for a state to be considered democratic. However, it is not a sufficient condition because comprehensive civil liberties must also be present for a state to be considered democratic. Nonetheless, the absence of competitive elections is a sufficient condition to qualify a state as non-democratic, as a democracy cannot exist without competitive elections.1

    Under the heading of CCM, we place four specific techniques: Qualitative Comparative Analysis using conventional, crisp sets (csQCA, often simply labeled QCA in the literature), multi-value QCA (mvQCA), fuzzy-set QCA (fsQCA), and MSDO/MDSO (most similar, different outcome/most different, same outcome).

    Box 0.1 About Terminology and Labels: QCA, csQCA, mvQCA, fsQCA, and Software

    QCA using conventional Boolean2 (or “crisp”) sets was developed first, which is why the label “QCA” is so often used to name this technique. In this volume, however:

    • We use the label QCA as an umbrella term that captures the three main types (Boolean, multi-value, and fuzzy set) as a group. After all, they share many commonalities (see Chapter p. 1).
    • When referring explicitly to the original Boolean version of QCA, we use csQCA3 (where “cs” stands for “crisp set;” see Chapter 3, p. 33).
    • When referring explicitly to the version that allows multiple-category conditions, we use mvQCA (where “mv” stands for “multi-value;” see Chapter 4, p. 69).
    • When referring explicitly to the fuzzy-set version, which also links fuzzy sets to truth table analysis, we use fsQCA (where “fs” stands for “fuzzy set;” see Chapter 5, p. 87).
    • Finally, we use fuzzy sets to designate the original fuzzy set analysis as developed by Ragin (2000).

    When referring to software, we use:

    • QCA-DOS to refer to the original program for crisp-set analysis developed by Charles Ragin and Kriss Drass.
    • TOSMANA to refer to the mvQCA program developed by Lasse Cronqvist.
    • FSQCA to refer to the fuzzy-set version developed by Charles Ragin, Kriss Drass, and Sean Davey.

    Note that all existing versions of QCA software (QCA-DOS, TOSMANA, and FSQCA) are capable of performing conventional crisp-set analysis of the type described in Ragin (1987) and De Meur and Rihoux (2002).

    These four techniques form the core of this book. Although these are quite specific techniques, we will also tackle broader issues that any social scientist inevitably confronts, no matter what methodology he or she uses—e.g., causality, operationalization, generalization, temporality, mechanisms, and process.

    Box 0.2 Goals of This Textbook
    • Provide a broad introduction to the comparative template: the purpose of systematic comparison and the key operations thereof (especially case and variable selection)
    • Present the main assumptions and underpinnings of the configurational comparative approach
    • Using one concrete example throughout, introduce the key operations and workings of four specific techniques: csQCA, mvQCA, fsQCA, and MSDO/MDSO
    • Examine the strengths and limitations of these techniques, and provide a critical overview of real-life applications produced so far
    • Provide useful resources and tips, and identify “good practices” for practitioners, so they can better exploit the potential of these techniques
    Structure of this Book

    After this introductory chapter, whose aim is to lay out the basic purpose and aims of this book, Chapter 1 presents the whole approach behind QCA and related techniques. First, this approach is discussed more at the epistemological level, with a key focus on “small- and intermediate-N” research situations. Some key features of QCA are also laid out: the interplay between theoretical and case-oriented knowledge, a specific understanding of causality and complexity, and particular goals when it comes to generalizing findings. We also present different ways to exploit QCA—it is indeed suited for several different purposes.

    In Chapter 2, we tackle issues of comparative research design and all the practical steps that need to be performed before QCA techniques (csQCA, mvQCA, or fsQCA) are actually implemented. The key practical questions deal with strategies of case selection, as well as model specification—especially the selection of the explanatory variables (called conditions). In this context, MSDO/MDSO is presented as a specific technique that can be used as a help in this challenging process of selecting cases and conditions. An empirical study is introduced as an example: a comparative analysis of the survival or breakdown of democracies in Europe during the inter-war period.

    Chapters 3 to 5 then present the three core QCA techniques: first crisp-set QCA (csQCA), followed by multi-value QCA (mvQCA), and finally fuzzy-set QCA (fsQCA)—this sequence follows the way the data are coded: from completely dichotomous (only [0] or [1] scores on all variables) to much more fine-grained ones. All three techniques are discussed from A to Z, along with their key practical steps. The specificity of each technique is also underlined, along with the more basic or more advanced uses. Throughout these three chapters, the same “inter-war project” data are used, so as to show which added value is brought by each of the techniques. Many good practices tips are given along the way.

    In Chapter 6, we provide a broad review of applications of these techniques, in many different fields, on different topics, and with different uses of the techniques. The real-life applications presented here have been selected because they exemplify some good practices, as well as the potential and limitations of the techniques. All the main steps of a QCA procedure are revisited in this way, from the prior steps of case selection and model specification to more advanced features such as the treatment of the so-called contradictory simplifying assumptions. Because of their specificities, csQCA, mvQCA, and fsQCA applications are discussed separately.

    Next, Chapter 7 addresses all the main critiques that have been issued vis-à-vis QCA and its different techniques. There are many of such critiques, from dichotomization to temporality, through case sensitivity and the use of non-observed “logical remainder” cases. For each one of these critiques, we discuss to what extent the critique is valid and, if it is, to what extent the difficulty or limitation can be technically addressed.

    Finally, in Chapter 8, we provide an open and prospective conclusion, as indeed QCA is an expanding and moving field. Some particularly promising paths are further discussed, such as using the different QCA techniques in a sequence or engaging in a fruitful dialogue (or confrontation) with other techniques, qualitative or quantitative. The last sections are devoted to specific topics on which some innovations can be expected or are already underway, particularly in terms of software development and more advanced uses of the techniques.

    At the end of the book, we have also gathered a set of key resources, such as a glossary, an extensive bibliography, author and subject indexes, and links to various resources on the Web.

    How to Read this Book

    This volume has been designed to follow a logical sequence, from general considerations to the presentation of specific techniques, then from these techniques to comments on applications, strengths, and limitations, with practical tips throughout. Readers who intend to engage in rigorous hands-on use of these techniques are thus best advised to read the whole textbook from beginning to end. Readers who wish to have a quick overview of the possibilities and key features of the techniques are advised to start from Chapter 6 (review of applications) and then to consult, in a selective way, sections from Chapters 3 to 5, depending on which pertain most directly to their specific research purposes. Six types of pedagogical resources have been inserted for all readers.

    Box 0.3 Pedagogical Resources in This Textbook
    • Chapter-opening summaries, listing the main goals of each chapter
    • Chapter-concluding summaries, listing key points as well as key complementary readings for each chapter
    • Illustrations and examples, to make technical aspects more accessible
    • Basic technical definitions, explicating specific terminology
    • “Good practices” boxes, listing key hands-on advice for using the techniques in appropriate ways
    • A glossary, an index, and a list of key resources, toward the end of the volume—and more on a specific “resources” Web page at
    Companion Readings

    Naturally, as this textbook covers a lot of ground in a relatively restricted volume, some key companion readings are recommended to get a more finegrained picture of QCA and its techniques. Although many volumes have touched upon QCA to some extent,4 there are three core companion volumes to this one. On the one hand, the two agenda-setting volumes by Charles Ragin, The Comparative Method (1987) and Fuzzy-Set Social Science (2000), lay out all the fundamentals and the overall ambition of QCA in its different variations. They have been recently complemented by a volume updating and extending the whole discussion around fuzzy sets (Ragin, 2008).

    On the other hand, the focus of the textbook by Schneider and Wagemann (2007 in German; forthcoming in English) is, altogether, more technical; it provides, in particular, detailed discussions on necessity and sufficiency, consistency and coverage, Boolean algebra and set theoretic operations, measurement, concept formation, advanced features of fuzzy sets, and more information on the use of software. As a contrast, our textbook is more broad and encompassing, at a more introductory-to-intermediate level. What's specific about this volume is that it provides a state-of-the-art, basic treatment of all three main QCA techniques as well as what is “upstream” (comparative research design in particular), an extensive discussion of the strengths and limitations as well as published applications in many fields and disciplines—all of this in a relatively compact format. Thus the more focused and technically elaborate Schneider and Wagemann textbook is to be considered as a complementary resource to our textbook—along with resources on the Web (see p. 179). A specific piece on good practices in QCA, by Wagemann and Schneider (2007; Schneider & Wagemann, 2008) as well, is also complementary to the advice offered in this book.

    For those who read French, the De Meur and Rihoux textbook (2002) can still be useful, in particular in its discussion of the Boolean foundations of QCA and in its visual representations of the data and key operations. The concise textbook by Caramani (2008) is useful for an in-depth look into the “black box” of QCA (technical aspects, fine-grained discussion of causation and control) and to reflect more thoroughly on epistemological and practical issues of comparative research design. Finally, two recent volumes are particularly helpful “upstream”: that of Goertz (2006b), for the practical stages of comparative research design, case selection, concept formation and measurement; and that of Gerring (2006), also for case selection and for a reflective view on how case-oriented knowledge and “case intimacy” can be gained before engaging in comparative analysis.

    Key Points
    • Comparison is a key operation in any empirical scientific effort.
    • This book is about specific comparative methods and techniques that enable systematic cross-case comparisons, while at the same time attending to within-case complexity, particularly in small- and intermediate-N research designs.
    • The “QCA” label designates the whole approach, and more specific labels (csQCA, mvQCA, fsQCA) designate particular techniques.
    • In QCA, complex cases are transformed into configurations: specific combinations of conditions linked to a given outcome.

    Key Complementary Readings

    Caramani (2008), Lijphart (1971), Ragin (1987, 2000, 2008), Schneider & Wagemann (2007, forthcoming).


    1. Example suggested by Lasse Cronqvist.

    2. “Boolean” simply means that variables can be coded only [0] or [1]—that is, they have to be dichotomized. See the subsection on dichotomization, p. 39.

    3. Note that, in most publications so far, csQCA has simply been referred to as “QCA”—hopefully, the more precise and unambiguous label “csQCA” will be used from now on.

    4. Quite a few other textbooks with a broader methodological purpose cover QCA to some extent. We particularly recommend the following three, because they put QCA (and its logical foundations) within a broader setting in a thoughtful way: Becker (1998), Pennings, Keman, & Kleinnijenhuis (1999), and Peters (1998).

  • Appendix: Further Resources for Configurational Comparative Methods
    Where to find Further Information

    There are two key locations where you will find further information and a large amount of resources on Configurational Comparative Methods. The first one, specifically as a companion to this textbook, is the textbook resource page (URL:, in which we have compiled more detailed information on many aspects and practical points presented in the textbook. It is designed to be a help for users, at all levels (from beginners to more advanced), working on their own applications.

    The second one is the COMPASSS international resource site (URL:, which also contains many useful resources. In particular, as you start working with CCM and QCA techniques, you will probably be mostly interested in the “didactics,” “working papers,” and “international bibliographical database” sections. There are also many links to other sites—for example, the FSQCA and TOSMANA pages, where you can freely download the programs.

  • Glossary
    Box G.1 “Good Practices” (13): Words Matter, So Use the Correct Terminology!

    It is crucial to use the correct QCA terminology when writing up a report, publication, and so on in order to:

    • Avoid confusing the reader, especially if he or she has been mostly trained in different methods and approaches
    • Reinforce the notion that QCA techniques are underpinned by a specific paradigm, with its specific goals, assumptions, and conception of causality (e.g., “conditions” are not “independent variables”)
    • Avoid being criticized on invalid grounds (e.g., a “minimal formula” is not a “general trend,” which could be statistically inferred from a sample to a whole population)
    • Be fully understood in your demonstration

    It might be useful, if space allows (in footnotes, for instance), to provide short definitions of the key QCA technical terms you are using. It is also advised to clearly mention the technique(s) you are using (csQCA, mvQCA, fsQCA, etc.) in your abstract.

    In this glossary, we have gathered key technical terms used in QCA and its techniques, along with concise definitions. Some equivalent terms, used by some authors in the literature, as well as by the FSQCA or TOSMANA programs, are also mentioned.

    Binary variable (equivalents: Boolean variable, dichotomous variable): variable that takes only two values: [0] or [1].

    Boolean distance: the number of Boolean (i.e., dichotomized, with [0] or [1] values) conditions by which two cases differ from one another.

    Boolean minimization: see Minimization.

    Complex solution: minimal formula derived without the aid of any logical remainders.

    Condition (equivalents: condition variable, causal1 condition): an explanatory variable that may affect the outcome. Note: It is not an “independent variable” in the statistical sense.

    Configuration: a combination of conditions relevant to a given outcome. It may correspond to one, more than one, or no empirical case(s). It corresponds to one row of a truth table.

    [−] outcome configuration: a configuration whose outcome value is always [−], indicating it could be [1] or [0]; also known as a “don't care” configuration.

    [0] outcome configuration: a configuration whose outcome value is always [0].

    [1] outcome configuration: a configuration whose outcome value is always [1].

    Consistency: the degree to which empirical evidence supports the claim that a set-theoretic relation exists. A subset relation may signal a necessary or a sufficient condition, depending on which is the subset, the cause (sufficiency), or the outcome (necessity).

    Contradictory configuration: a configuration whose outcome value is [1] for some cases and [0] for other cases. It therefore covers a set of empirical cases, which, although they share the same set of condition values, display different outcome values.

    Contradictory simplifying assumption: when the same logical remainder is used both in the minimization of the [1] outcome configurations and in the minimization of the [0] outcome configurations, thereby making two contradictory assumptions regarding the outcome value of that logical remainder.

    Coverage: an assessment of the way the respective terms of the minimal formulas “cover” observed cases (three types of coverage: raw coverage, unique coverage, and solution coverage).

    Fuzzy set membership score: the degree to which a given case belongs to a set, which can be any value between two qualitatively defined states: full membership (1) and full nonmembership (0) in the set.

    Implicant: see Prime implicant.

    Intermediate solution: minimal formula derived with the aid of only those logical remainders that are consistent with the researcher's theoretical and substantive knowledge.

    Interval level (of measurement): quantitative data that are ordered on a constant scale, with equivalent differences between values; an interval scale with a meaningful zero point is known as a ratio scale.

    Logical remainder (equivalents: logical case, logical remainder case, remainder, counterfactual, non-observed case): a configuration (combination of conditions) that lacks empirical instances. Logical remainders may be included in the Boolean minimization.

    Membership score: see Fuzzy set membership score.

    Minimal formula (equivalents: reduced expression, minimal equation, solution):

    formula obtained through Boolean or set-theoretic minimization. It typically consists of a reduced set of prime implicants (terms), connected by the Boolean “OR” [+] operator, also known as a “sums of products” expression.

    Minimization (equivalents: Boolean minimization, Boolean synthesis, Boolean reduction): the process of reducing, through Boolean or set-theoretic algorithms, complex expressions into a minimal formula.

    Necessary condition: see Necessity.

    Necessity: a condition is necessary for an outcome if it is always present when the outcome occurs, and if it is never absent when the outcome occurs (thus the outcome cannot occur in the absence of the condition). The outcome is a subset of the cause.

    Nominal level (of measurement): the data are classified, but not ordered (e.g., religion, gender [male/female]).

    Ordinal level (of measurement): the data are ordered, but the differences between the values or ranks are not equal (e.g., social class, rank order of preference for political parties).

    Outcome (equivalent: outcome variable): the variable to be explained by the conditions; usually the outcome is the main focus of a study.

    Parsimonious solution: minimal formula derived with the aid of logical remainders, without any evaluation of their plausibility.

    Prime implicant: reduced expressions derived in the course of Boolean minimization. Typically, a subset of the prime implicants that are derived constitute a minimal formula, the endpoint of Boolean minimization. A prime implicant is usually a set of conditions joined by the Boolean “AND” [*] operator. Each prime implicant in a minimal formula covers a series of configurations from the truth table with a given outcome.

    Property space: the analytic frame that is defined by a given set of conditions; with fuzzy set, it is a multidimensional vector space defined by the fuzzy-set conditions. The corners of this multidimensional vector space correspond to truth table rows.

    Remainder: see Logical remainder.

    Set: any collection of distinct objects (called members) considered as a whole. A set can be described by certain properties or characteristics.

    Simplifying assumption: assumption made on the outcome value of a logical remainder, so it can be included in the minimization procedure, in order to obtain a simpler minimal formula.

    Solution: see Complex solution; Intermediate solution; Parsimonious solution.

    Subset relation: with crisp sets, a subset relation exists whenever all the members of one set are contained within another set; with fuzzy sets a subset relation exists whenever membership scores in one set are consistently less than or equal to membership scores in another set.

    Sufficiency: a condition (or combination of conditions) is sufficient for an outcome if the outcome always occurs when the condition (or combination) is present (however, the outcome can occur for other reasons as well). In short, the cause is a subset of the outcome. Most terms in minimal formulas (e.g., the term AB in the minimal formula AB + CD → Y) constitute subsets of the outcome and therefore can be interpreted as sufficient (but not necessary) for the outcome.

    Sufficient condition: see Sufficiency.

    Term: an element within a Boolean sum. In Boolean or set-theoretic expressions, it is usually a combination of conditions joined by the Boolean “AND” [*] operator (set intersection).

    Truth table (equivalent: table of configurations): synthetic display of all configurations (combinations of conditions) based on a given data set.

    Venn diagram: a graph showing all the possible mathematical or logical relationships between sets.


    1. It is, however, recommended to be cautious in the use of the “causality” terminology, unless you have a clear view of the causal mechanisms at work in your field of study.

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  • About the Editors

    The editors and contributors of this textbook are all active in the COMPASSS research group (

    Benoît Rihoux is professor of political science at the Centre de Politique Comparùe of the Université catholique de Louvain (Belgium). His substantive research interests include political parties, new social movements, organizational studies, political change, environmental politics, and policy processes. He is coordinator of the COMPASSS research group and oversees the management of its linked Web pages, databases, and archives. He is also joint convener of international initiatives around methods more generally, such as the ECPR Standing Group on Political Methodology, the ECPR Summer School in Methods and Techniques, and the ECPR Research Methods book series (Palgrave; editor, with B. Kittel). In connection with the topic of this book, he has also recently published Innovative Comparative Methods for Policy Analysis: Beyond the Quantitative–Qualitative Divide (Springer/Kluwer, with H. Grimm, 2006).

    Charles C. Ragin holds a joint appointment as professor of sociology and political science at the University of Arizona. In 2000–2001 he was a Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University, and before that he was professor of sociology and political science at Northwestern University. His substantive research focuses on the welfare state in advanced industrial societies, comparative ethnic political mobilization, nationalism, and international inequality. His books include The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies (University of California Press, 1987), Issues and Alternatives in Comparative Social Research (E. J. Brill, 1991), What Is a Case? Exploring the Foundations of Social Research (Cambridge University Press, with Howard S. Becker, 1992), Constructing Social Research: The Unity and Diversity of Method (Pine Forge Press, 1994), Fuzzy-Set Social Science (University of Chicago Press, 2000), and Redesigning Social Inquiry: Fuzzy Sets and Beyond (University of Chicago Press, 2008).

  • About the Contributors

    Dirk Berg-Schlosser, PhD in political sciences, is professor at the Institute of Political Science at the University of Marburg (Germany). His major areas of research and teaching include comparative politics, African and Third World politics, political culture, democratization, and comparative methodology. He was chair of the European Consortium for Political Research (ECPR) from 2003 to 2006 and has since 2006 been vice president of the International Political Science Association (IPSA).

    Damien Bol is a fellow researcher at the National Fund for Scientific Research (FNRS) and at the Centre de Politique Comparùe of the Université catholique de Louvain (Belgium) and maintains the COMPASSS research group Web pages and linked databases. His research focuses on political and electoral institutions (especially change and choice), approaches and techniques in comparative politics (especially those dealing with “small populations” and with the time dimension), political representation, and strategic interactions between political players

    Lasse Cronqvist, PhD in political sciences, is lecturer at the Institute of Political Science at the University of Trier (Germany). He teaches German and comparative politics as well as social science methods. His research focuses on comparative methodology and comparative policy studies. He has also authored software for Configurational Comparative Analysis (TOSMANA).

    Gisèle De Meur is professor of mathematics at the Université Libre de Bruxelles (Belgium). She teaches mathematics for social scientists, methodology, introduction to computing, and epistemology and is director of the Laboratory for Mathematics and Social Sciences (MATsch) at the ULB. Her more recent research interests include study of “pseudoscience” discourse, geometric patterns in arts, mathematical models applied in anthropology, as well as political science, methodology (quali-quantitative comparative analysis), and gender studies.

    Sakura Yamasaki, PhD in political sciences, was a Fellow Researcher at the National Fund for Scientific Research (FNRS) and at the Centre de Politique Comparùe of the Université catholique de Louvain, Belgium. She has elaborated and maintained the COMPASSS research group Web pages and linked databases since their launching (2003–2007). Her areas of interest include comparative methodology (especially QCA), network analysis, nuclear energy policy, policy change and new social movement theories. She is now working in the energy trading sector.

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