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© (2008) Swiss Political Science Review 14(2): 315–56

In Search of Co-variance, Causal Mechanisms or Congruence? Towards a Plural Understanding of Case Studies Joachim Blatter and Till Blume

Universitiy of Rotterdam/University of Lucerne and University of Konstanz Methodological reflections about case study research have increased within recent years. According to our account, there are three distinct approaches to case studies: co-variational, causal process tracing, and congruence analysis. The main goals of this article are to lay out the distinct ways in which causal inferences are drawn for the cases under study and to scrutinize the different understandings and directions of generalization within these three approaches. By doing so we highlight two aspects: First, causal process tracing and congruence analysis should be seen as two distinct alternatives to the dominant co-variational template. Second, the main characteristics of case studies, their thickness, provides only an unavoidable dilemma if we aim to generalize the findings towards a wider population of similar cases as in the co-variational template. If we would like to get deeper insights – as the causal process tracing approach does – or if we would like to use the empirical evidence for a broader theoretical discourse – as the congruence analysis does – case studies do not face a trade-off. Keywords: Case Study Methods • Co-Variation • Process Tracing • Congruence Analysis • Causal Configuration

Introduction “In the terms many use to describe it, the comparative method is essentially the statistical method writ small” (Hall 2006: 26). For comments on earlier versions of this paper we would like to thank John Gerring, Jack Levy, Gary Goertz, Markus Haverland, Adrian Sinkler, Nivien Saleh, Ingo Rohlfing and further participants of the sessions at the 103rd Annual Meeting of the American Political Science Association, Chicago, August 30–September 2, 2007 and at the 4th ECPR General Conference, Pisa, September 6–8, 2007. The two anonymous reviewers of the SPSR provided further stimulating comments and helpful suggestions.

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Joachim Blatter and Till Blume

In the past few years we have witnessed an unprecedented methodological reflection on case study research in Political Science. Alexander George and Andrew Bennett’s work, “Case Studies and Theory Development in the Social Sciences” (2005), and John Gerring’s book, “Case Study Research – Principles and Practices” (2007a), are milestones that revealed many specific characteristics as well as the qualities and difficulties of case study research. Nevertheless, both books might only mark the beginning of an intensive methodological reflection on case study research in the discipline. A comparison of these two books made clear that there are different understandings – or at least different emphasis – on how to pursue case study research (Blatter and Blume 2008). In the following, we would like to contribute to the ongoing methodological reflection on case study research by delineating three distinct types, styles or approaches to case studies. The first one, which we label co-variational (COV), corresponds to the dominant perspective on (comparative) case study research in Political Science, most recently and coherently laid out by Gerring (2007a). For our purposes, this type serves as an established template that provides core elements and characteristics of doing case study research for which we need to identify the equivalents in the two alternatives. The second type, causal process tracing (CPT), has also now claimed a lot of attention as a result of the differentiation between “dataset-observations” and “causal-process observation” in the Brady and Collier volume (2004) and the emphasis of causal process tracing in the book by George and Bennett (2005). Some elements of the third type, which we call congruence analysis (CON), can also be found in George and Bennett (indeed, they lay out the “congruence method”, as they call it, prior to causal process tracing). However, we go beyond their understanding of the congruence method in many ways and conceptualize it as a strongly theory-centered alternative to the variable-centered COV. In contrast, CPT focuses on the temporal unfolding and dense interactions of causal factors in specific cases and is very much case-centered. In consequence, we assign many features, which have been described as “causal-process obWe use the words types, styles and approaches interchangeably. The three types of case studies are not entirely distinct categories. Due to their overlapping nature, they follow Max Weber’s understanding of ideal types and are in line with the recent trend in concept formation methodology which stresses the importance of “family resemblance categories” or “radial categories”, instead of classical categories with clear boundaries (Collier and Mahon 1993; Davis 2005: 31–42, 78–80; Goertz 2006: 69–94).

In Search of Co-variance, Causal Mechanisms or Congruence?

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servation” or placed under the already popular label “process tracing”, to congruence analysis. The three styles of case study research have affinities to specific ontologies and corresponding epistemologies. This means that, although there is an inherent-logical and empirical-practical tendency towards specific ontologies/epistemologies, there is no necessary connection (for a similar understanding of “affinity,” see Gerring 2004). Nevertheless, we want to stress that all approaches are located in what is usually called the “middle ground” between naive positivists and radical constructivists (e.g. Davis 2005: 81). All approaches acknowledge the concept- or theory-dependency of empirical observations without abandoning the belief that we can use empirical observations as proof for the correctness of propositions or for checking the relevance of concepts and theories for creating useful meanings in the empirical context. Although we acknowledge that methodology is and should be based on ontological and epistemological foundations, we would like to avoid being caught up in fundamental philosophical debates. Instead, we would like to contribute to a more pragmatic search for methodological advice for doing good case study research. Therefore, we concentrate in our description of the three styles of case-study research on their ways to draw inferences from concretes/observables to abstracts/unobservables and in respect to their understanding and direction of generalizing the findings beyond the cases under study. These aspects have strong practical implications for both, the kinds of observations looked for and for the appropriate ways of selecting cases – and theories. The essay proceeds as follows: First, we delineate the differences between the three types of case-study research in respect to the ways to draw inferences for the cases under investigation (section 2). Since our argument that there are two (and not just one) alternatives to the co-variational approach is a “risky proposition”, which runs contrary to most attempts to scrutinize such an alternative (see for example Checkel 2005; Hall 2006; Schimmelfennig 2006), we add an extra subchapter on the differences between CPT and CON. In section 3, we scrutinize the different understandings and direction of generalization of the three types. We argue that COV is aiming to draw generalizing conclusions from cases to a wider population, whereas CPT strives to get deeper and denser insights, and CON is used in order to address a broader theoretical discourse. Furthermore, we propose that case studies are characterized by their thickness, defined as multiple and diverse observations per case plus intensive reflection on the

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congruence/resemblance between concrete empirical observations and abstract theoretical concepts. We end up with the main insight, that there exists a trade-off between this basic characteristic of case studies (thickness) and the goal of COV, but not between the thickness of case studies and the goals of CPT and CON. In conclusion, we argue that it is necessary to develop similarly coherent and consistent methodological advice for causal process tracing and congruence analysis, as has been done for the co-variational approach. This is even more so because only the former two approaches can overcome the logical conclusion which can be drawn from a COV approach that case studies are just second best solutions or secondary additions to large-N studies. Different Ways to Draw Inferences for the Cases under Investigation We understand the term “inference” as being able to draw conclusions from empirical observations to abstract causal explanations and interpretations for the case(s) under investigation. There are different ways to draw inferences (Table 1). Inferences through Co-Variation In a co-variational approach, causal inferences are drawn on the basis of observed co-variation between causal factors (independent variables) and causal effects (dependent variables). If there exists such a covariance over time or space between the independent variable (X) and dependent variable (Y), we can infer that X caused Y (the preconditions for these conclusions are addressed below). Such an approach has an affinity to focus on the effects of specific causes and not on the causes of specific effects – like quantitative research (Mahoney and Goertz 2006: 230–231). This affinity becomes most obvious when Gerring explains the search for internal validity of causal claims in case study research with the help of the “experimental template.” The researcher tries to find out what difference a specific “treatment” (the existence of a specific cause) makes for the outcome of a social process (Gerring 2007a: 152–172). Furthermore, it is important to realize that “observations” are confined to “data-set observations”: a set of scores for all dependent and independent variables put into


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Table 1: Different ways to draw inferences. Inference

Description

Co-Variation

1. Control of other varConcrete Observations: Co-Variation (over time or space) among indi- iables. cators of the dependent variable (Y) and indi2. Theoretically decators of an independent variable (X). duced hypothesis for causal direction. Abstract Conclusions: X has a causal effect on Y.

Causal Process Concrete Observations: Tracing Temporal unfolding of situations, actions and events, traces of motivations (or other lower level mechanisms), evidence of (complex) interactions between causal factors, and/or information about restricting/catalyzing contexts/conditions, and detailed features of a specific outcome. Abstract Conclusions: Actual working of a causal mechanism/ actual interaction between the elements of a causal configuration. Congruence Analysis

Concrete Observations: (Mis-)Matches between empirical findings and concrete expectations deduced from core elements of theories: E.g., central actors and structures, traces of motivational foundation of (inter)action, specific features of X and Y, co-variance among indicators of X and Y. Abstract Conclusions: Relevance/relative strength of theories to explain/understand the case(s).

Preconditions

1. “Smoking gun”-observations. 2. A full “storyline” with density and depth and an “authentic” and fine-grained “picture” of events within their contexts.

1. Plurality of fullfledged and coherent theories from which concrete expectations can be deduced. 2. Plurality and diversity of available observations.

a row of a rectangular data-set (Gerring 2007a: 20, 23–25; Seawright and Collier 2004: 283). Two conditions are necessary for drawing causal inferences by looking at co-variation of independent and dependent variables. First, other factors which could have influenced the outcomes must be “controlled for”. Since in small-N-studies this cannot be done by statistical means, researchers generally select “comparable cases” (Lijphart 1975) or try to find “most similar systems” (Przeworski and Teune 1970). Second, the co-variational relationship between two variables must be given a “meaning” by con-

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necting the empirical observations to theory. Whereas good co-variational research is based on full-fledged theories, variable-centered research very often deduces the hypotheses specifying the causal direction between dependent and independent variable by ad-hoc arguments or by reference to prior empirical findings. Indeed, the co-variational approach is not dependent on a full-fledged theory because the hypothesis only must define the dependent and the independent factor and contain a prediction about which value of the independent variable causes a specific outcome at the dependent variable. An expanded version goes beyond the specification of the direction of the relationship between the two variables and provides a specified theoretical model why and how the independent variable has a causal effect on the dependent variable. Reasons, structures, mechanisms or motivations which link the causes to the effects are deduced from theories but they are not traced empirically. In other words, the co-variational approach collects data of values of independent and dependent variables and draws logical conclusions from this data to the existence or non-existence of an influence of the independent variable to the dependent variable. This means that this approach – as an ideal-type – does not attempt to observe the actual causal process, only the “input” and the “outcomes”. Furthermore, it reduces the relationship between conceptual framework and concrete observations to what is called “operationalization” of the variables – an ex-ante specification of indicators and scales for measuring the empirical values or scores of the variables. Inferences through Causal Process Tracing Those who propose CPT are not satisfied with inferring the existence of a causal relationship by deductive reasoning and by observing only the scores of the independent and dependent variables. They want to get a closer grip on causal processes by tracing causal mechanisms, complex interactions of causal factors, and by delineating causal pathways. George and Bennett (2005: 138) state: “Process-tracing is an operational procedure for attempting to identify and verify the observable within-case implications of causal mechanisms”. Within-case implications of causal mechanisms include the values of independent and dependent variables, but go beyond these types of observations and try to identify traces for every step between the cause and the outcome. Causal mechanisms are seen as “ultimately unobservable” (George and Bennett 2005: 137) – but one can observe their traces. George and Bennett explain that “(i)n contrast to approaches that empha-


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size causal effects […], which draw on regularity of association and congruity of magnitude as sources of causal inference, explanation via causal mechanisms also draw on spatial contiguity and temporal succession […]. In particular, explanation via causal mechanism involves a commitment in principle to making our explanations and models consistent with the most continuous spatial-temporal sequences we can describe at the finest level of detail that we can observe […]. Mechanism-based explanations are committed to realism and to continuousness and contiguity in causal processes” (2005: 140). From our perspective it is important to specify – more precisely than George and Bennett – what we are looking for when tracing causal processes. Before we get to internal differentiations within the CPT approach, it is important to highlight its core aspect: If we take the term causal process tracing serious, it is the temporal dimension of causality taking center stage in this form of drawing inferences. With this in mind, we can differentiate two different concepts for which CPT is the best available technique to trace the observable implications (Blatter and Blume 2008): causal mechanisms and causal configurations. (1) Causal Mechanisms.–––For a precise understanding of causal mechanisms, we should reserve this term to causal factors involving lower levels of analysis in comparison to the level of analysis on inputs and outcomes of a causal process are measured. Adherents of mechanism-based social science differentiate between “situational mechanisms”, “actionformation mechanisms” and “transformational mechanisms” (Hedstroem and Swedberg 1998: 22, similarly Esser 2002). The second type of these mechanisms is based on behavioral micro-foundations (or in other words: on a theory of (inter-)action, but not necessarily rational choice theory); the first and the third types represent mechanisms linking different levels of analysis. All three types are linked together in a logical order of temporal succession. Whereas a good co-variational approach explicates and forThis is often overlooked by those who want to press all alternatives to the co-variational template under the heading of “process-tracing”. The term “process” refers to the object of the observation and not to the process through which the scholar tries to get to valid conclusions (through some kind of Bayesian updating, for example).

In contrast, the aspect of “spatial contiguity” is problematic. Although spatial contiguity has become a boost as important factor in explanatory strategies through “agent-based modeling” (Axelrod 1997), we would only accept spatial contiguity as an important element in causal process tracing when it is interpreted in a much broader sense (e.g. by taking elements like “distance”/ “proximity” in network analysis also into account).

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malizes the working of these mechanisms based on theory and/or prior empirical knowledge, a process-tracing approach actually tries to find traces of these mechanisms below the original level of analysis within the cases under investigation. Usually, this means to search for information about the perceptions and motivations of actors (including self-conceptions and unconscious triggers of activity) and about unintended consequences. Such a search for empirical traces of mechanisms beyond “data-set-observations” is especially warranted if the theoretical models and mechanisms are not fully deterministic (e.g. Elster 1998, similarly Davis 2005: 132–153 for norm-based behavior). (2) Causal Configurations.–––The term causal configuration should be used if we start with a more holistic ontology than variable-based ones and if we assume the existence of intense links and/or complex interactions between various causal factors in the production process of an outcome (Ragin 2000: 64–119). In contrast to causal mechanisms, the individual elements of a causal configuration are conceptualized on the same level of analysis. Such an ontological starting point has a strong affinity and can easily be integrated with the methodological reflections on necessary and sufficient conditions and set-theoretical logic (see Goertz and Starr 2003, Ragin 2008). In consequence, the concept of causal configuration includes at least the following three forms: First, interaction effects, in the sense that it is not the simple sum of the causal strength of two or more causal factors which makes them strong enough to produce an outcome, but their co-existence which accelerates (or moderates) their causal power; second, the idea that specific causal factors (sometimes confusingly called causal mechanisms) work only within specific contexts; and third, the idea that the working of a first causal factor is a necessary precondition for the activation of the second in a later stage. In terms of temporality, the first two types of causal configurations represent “causal conjunctures” (see Pierson 2004) and the last type a “causal chain” (see Goertz and Levy 2007). In terms of assumed forms of causation, the first type accommodates recursivity, whereas the two latter forms correspond to the asymmetrically deterministic logics of necessary and sufficient conditions.

From a point of pure logic, such a context factor is nothing else than a necessary condition for the working of the main causal factor. Each of them is necessary in order to be together a sufficient condition/causal configuration for the outcome.


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(3) Examples.–––Theda Skocpol’s study on social revolutions (1999) is not by accident the case study on which almost all case study methodologists exemplify their analytical approaches, since she uses many techniques to make her descriptions and explanations plausible – and not just the application of Mill’s co-variational methods which she explicitly mentions (Mahoney 1999). Mahoney’s recapitulation of her arguments reveals that she draws on the logics of causal chains and causal conjunctures. Skocpol’s explanation of the three revolutions in France, Russia and China is primarily based on a conjunctural argument. She claims that two general factors had to come together in order to lead to a social revolution: state breakdown and peasant revolutions. Only the fact that both factors came together at the same time made social revolutions possible. On a lower level of her analysis, she applies not only the logic of conjunctural causation but also the logic of causal chains. For example, state breakdowns are explained by three main factors: agrarian backwardness, international pressure and the existence of a non-autonomous state, which could not put forwards any major reforms. If we follow the recapitulation of Mahoney (1999: 1166–67), specific elements of these three factors sometimes may work in conjunction, but one factor may also be seen as a (temporary) precondition for the working of the other factors or there exist complex interactions between all three factors. It seems worth-wile to note that Skocpol does not provide a sophisticated theoretical base for every step in her causal chain, nor does she refer to causal mechanisms. Furthermore, she also does not always provide an explicit counterfactual thought experiment in order to prove that the identified cause was necessary for the next step. She makes her causal claim mainly on the basis of thick historical description and narratives (Mahoney 1999). We take this prominent example to bolster our assumption that the main practical technique to show the interaction or dependencies of causal factors in causal conjunctures and causal chains is not the counterfactual thought experiment (which still inhibits the co-variational logic although it is not based on observations), but “smoking-gun observations” (see below) and “narratives” with a dense storyline, deep insights into the strucWe do not provide an empirical example for tracing causal mechanisms here because of place restrictions. A sophisticated example for process tracing with this goal can be found in the work of Jeffrey T. Checkel (2005 for his methodological reflections).

Nevertheless, the counterfactual thought experiment is the logically most adequate technique if we assume configurational causation in terms of necessary and sufficient conditions (see Goertz and Levy 2007).

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tures and motivations of (individual, collective or corporate) actors and a fine-grained picture of the critical moments in which various factors came together to produce an important outcome (when this outcome inhibits the characteristics of path-dependency it is called “critical juncture”, see Pierson 2004). We want to point to another example which already has become famous among proponents of CPT, not only because it shows that CPT can produce more convincing results in comparison to statistical techniques, but because it shows that the focus on timing and the directedness of time provides a similar systematic basis for “causal process observations” (Seawright and Collier 2004: 277–78) than standardization does for “dataset observations” (Seawright and Collier 2004: 283). Brady (2004) reanalyzed the consequences of the fact that at the presidential Election Day in the year 2000, TV networks declared Al Gore the winner in Florida before the polls had closed in the panhandle counties. His goal was to show that causal process tracing could provide quite different results about these consequences than regression analysis based on data-set observations. With a chain of arguments focusing on the time of voting, Brady shows that the vote losses which George Bush suffered because the networks declared Al Gore the winner in Florida before the polls had closed in the panhandle counties were much lower than it was implied by Lott, who had argued on the basis of a regression analysis (between 28 and 224 in contrast to 10,000). Brady reaches a different conclusion mainly by using several diverse pieces of evidence to make clear that an overwhelming majority of voters had already voted before the TV networks declared Gore to be the winner in Florida. According to Gerring (2007a: 177), “Brady’s conclusion did not rest on a formal research design but rather on isolated observations […] combined with deductive inferences.” This characterization, in our view, devaluates “causal process observations.” Just because the observations are not “standardized” in the sense of data-set-observations, this does not mean that they are “isolated.” Instead of standardizing them, Brady orders his observations and arguments according to a temporal logic. Nevertheless, it is certainly true that reflections on the relevance and adequacy of various causal-process observations (e.g. short-term versus long-term Maybe it is necessary to stress at this point our “ideal-typical” style of argumentation again. We perceive smoking-gun observations and narratives as techniques which correspond logically most clearly to the ideal-type approach causal process tracing. This does not mean that in real case-study research counterfactual thought experiments cannot be used within CPT.


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processes) have not produced a set of methodological tool-kits in a similar way as it was done for the relevance and adequacy of data-set observations (e.g. reflections about functional equivalent indicators within different contexts). Inferences through Congruence Analysis What we call congruence analysis (CON) is an approach that focuses on drawing inferences from the (non-)congruence of concrete observations with specified predictions from abstract theories to the relevance or relative strength of these theories for explaining/understanding the case(s) under study. In order to be able to draw inferences about the relevance of theories it is necessary that the researcher reflects intensively on the relationship between abstract concepts and concrete observations. This can be done deductively or inductively; practical research usually includes both in an iterative process. Deductively, the researcher generates ex-ante predictions about what observations of the world will appear according to these theories. Inductively, the researcher reflects on which theory makes (more) sense for a specific observation. More than the other two approaches CON relies on the application of a plurality of theories. The main mechanism of control in this approach is the rivalry between various theories. Whereas the co-variational approach applies a strict understanding of congruence and coherence for a limited set of observations (data-set observations in which the values of the dependent and the independent variables have to correspond precisely with the hypothesized values), CON is much more open to a less strict understanding of congruence and coherence but uses a much broader set of predictions and observations.10 Whereas the CPT approach bases inference on the (relative) certainty of observations and on the internal consistency of the explanations within the case(s) under study, CON relies on the discriminatory power of specific observations and on the competition between internally coherent theoretical frameworks. We will address these aspects in more detail in the following. The term “prediction” is closely connected to deductive analysis, but does not refer – as in mainstream quantitative research – only to predictions for the value of the dependent variable (given a specific value of the independent variable), but to broader range of expectations derived from theories for the cases under study (cf. Seawright and Collier 2004: 284).

In consequence, it does not adhere to strict falsificationalism and H0 does not play any meaningful role in CON. 10

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(1) A Broad Spectrum of Predictions/Expectations Deduced from Theoretical Frameworks.–––What makes CON different from COV (and also different from the “congruence method” described by George and Bennett 2005: 181–204) is the fact that deductions are not limited to the expected co-variation between dependent and independent variable. Instead, those derived from theory should be as diverse as possible and should include data-set observations (values of independent and dependent variables) and causal process observations (e.g. sequences of actions and events). Predictions can and should include assumptions about the most important (individual, collective and/or corporate) actors, their perceptions and their motivations (traces of micro-foundational causal mechanisms), the corresponding structural factors (e.g. enforceable rules influencing strategies or meaning-generating discourses influencing perceptions) or other fundamental elements of the theory. In order to be able to deduce these kinds of predictions, theories must be conceptually rich. They must go beyond a hypothesis, which would just predict a specific causal relationship between a factor and an outcome, and they have to be open to complex conceptualizations of the dependent and independent variables. The latter is the case when the dependent variable consists of ideal types defined by multiple dimensions, which are theoretically consistent, but which cannot be aggregated into one score in a one-dimensional measurement scale (index). (2) Interpretation Instead of Operationalization.–––Linking abstract concepts to concrete (potential) observations is at the very heart of CON. CON is taking recent insights in the literature on concept formation into account, which stresses the fact that there are no point-to-point relationships between an objective/external world and an abstract meaningful concept. Contrary to earlier approaches of concept formation, it became clear that it is not adequate to define abstract concepts through a set of necessary attributes that can be observed empirically. Meaningful abstract concepts often have fuzzy boundaries. The meaning of concepts is not determined through the set of internal attributes/properties but through their embeddedness in a theoretical context (Davis 2005).11 This reveals that the attribution David Collier and his colleagues (Collier and Mahon 1993, Collier and Levitzky 1997), as well as Gary Goertz (2006) relax the strictness of the boundaries in comparison to the classic concept formation literature (Sartori 1984) with reference to Wittgenstein’s idea of “family resemblence” but still try to define theoretical concepts with reference to observable attributes. Mark Bevir and Asaf Kedar (2008) provide a radical “anti-naturalist” critique. In principle, we agree with Bevir and Kedar, in that we think that abstract concepts can only be defined in a meaningful way with reference to theory and not with reference to 11


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of observations to specific abstract concepts and to theoretical frameworks is neither easy nor can it be done in a clear-cut and purely technical manner.12 Researchers applying CON invest much more time and intellectual energy (resulting in explicit and extensive justifications) on this element of the research process in comparison to what is usually done during this phase in COV oriented studies (what is aptly labeled “operationalization”, i.e. the specification of indicators for variables). To be more precise, the metric quality of the indicator is important in COV-oriented case studies (in order to be able to compare adequately the variation of values of a variable between the cases). In contrast, the “only” relevant criterion for deduced indicators within CON is the concept validity of these indicators, the question whether the predicted observations express the meaning of the abstract conceptualization in a correct manner. This sensitivity for making inferential leaps between concrete observations and abstract concepts/meanings put interpretative techniques into the center of a CON approach. (3) Deductive and Iterative Interaction Between Abstract Concepts and Concrete Observations.–––To say that CON starts with theory might be misleading. The recommendation that the researcher should derive predictions about observations before conducting the empirical work is only justified as a means to enhance reliability and inter-theoretical fairness (“objectivity”). Such a purely deductive approach is only necessary if one is interested to evaluate the predictive power of theories with the help of statistical tools for which we need standardized observations (see the first example below). A more qualitative approach would allow for a more iterative interaction between theoretical implications and empirical indications. This makes it possible to use the full richness of information related to the empirical case to draw inferences about the relevance of theoretical concepts. observable attributes. However, we draw quite different conclusions. Instead of proposing a fundamental, non-commensurable dichotomy between natural sciences and social science, we think it is more fruitful to move the inferential leap between empirical observations and abstract concepts into the center of methodological reflection. The Committee on Concepts and Methods of the IPSA (see: www.concepts-methods.org) evidences that we are not alone in promoting the combination of methodology and concept formation as a fruitful alternative to the emphasis on multiple methods, a direction that the renamed Organized Section for Qualitative and Multi-Method Research of the APSA seems to follow. For those who think that meaningful concepts always have fuzzy boundaries it might be adequate to exchange the term congruence in congruence analysis with “resemblance” (see Davis 2005: 82–91). 12

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(4) The Weight of Specific Predictions.–––We can distinguish between three different approaches in respect to the question of how to weight the various predictions/observations when the researcher draws conclusion from the (non-)congruence of predictions and empirical observations for the relative strength or relevance of the theories. A first approach (of which we provide an example below) counts and weighs every match and every mismatch between prediction and observation equally. Others would argue that some predictions are more important than others. Some point to the predicted co-variation between dependent and independent variables as the central observation also for a CON approach (e.g. Hall 2006: 27). This highlights the commensurability of COV and CON, but inhibits the danger that CON is seen only as a secondary complement to COV. For us, it would be most consistent with an ideal-typical CON approach to give most weight to the conceptual core of a theoretical framework. This is usually not the outcome – or to put it more precisely: the necessary outcome given specific values of the causal factors in a fully specified model13 – but the empirical relevance of the major conceptual elements within a specific framework (e.g. fundamental background assumptions, central structures and basic motivations or other traces of the underlying micro-foundation of a theory). This is especially the case if the applied theoretical frameworks go beyond the confines of one specific paradigm or one research program (see the example we provide in the next section, Blatter 2008).14 (5) Examples.–––Wilson and Woodside (1999) call their approach a “degrees of freedom analysis” (DFA) because they ground their approach on Campbell’s famous article (1975), in which he showed that case studies could overcome the “degrees of freedom problem” because they embody a large set of different observations. They develop their analytical technique in order to compare the extent to which four theories of group decisionmaking are manifest in organizations. “The essence of the technique is the An impressive example for a study, which shows that a specific theoretical approach can be massively undermined by case studies although the final outcomes are in line with that theory, is Sagan’s study The Limits of Safety (1993). 13

Another rationale for assigning different weights to different observations could be based on prior expectations about the probability of the observation. Nevertheless, within a theory-centered CON-approach such Bayesian reasoning should be not applied on the level of individual observations but on the level of entire theories. We will come back to this when we explain the understanding of generalization within the CON approach as well as adequate logics for selecting cases and drawing conclusions beyond the cases in the next section. 14


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idea of “pattern matching” between theoretical propositions and observations in a set of data. […] The heart of DFA is the development and testing of a ‘prediction matrix’ […]” (Wilson and Woodside 1999: 216–17). Therefore, they start with deducing predictions from four theories of organizational decision-making (rational, bounded rational, political, garbage can) within seven basic decision phases (problem definition, solution search, data collection, analysis, use, information exchange, individual preference formation, evaluation criteria, and final choice between alternatives). For every phase they formulate two questions and answers in accordance with the four theories. For example, in the second phase (search for alternative solutions) they ask: Are potential solutions considered simultaneously and are they compared with each other? Whereas the rational model would be confirmed if the empirical observation provides a “yes” to this question, the political model and the garbage can model would be confirmed if the empirical data indicates a “no”. For the bounded rational model, the authors deduce an in-between category. They would confirm the theory if the empirical observations indicate that such a simultaneous consideration of solution occurs “partially.” Over all, their prediction matrix consists of 56 predictions. Once the prediction matrix was established, in-depths interviews were held with two or three members of four different organizations (buying centers across a university). The interviews focused on the same type of decisions but were semi-structured and included open questions. This means that the acquired data was qualitative in nature. In addition to the transcripts of these interviews, the researchers collected other documents with relevant information for the decision making process (Wilson and Woodside 1999: 219). Three “judges” extracted information out of the acquired data, which was seen as relevant to the specific cells of the prescription matrix. This information was then coded as hits and misses of each theory. Finally, Wilson and Woodside analyzed the results of the congruence analysis of all three judges with the help of statistical tests (chi-square and z-test). This means the quality of each theoretical model was tested by the fits of its predictions with the empirical observations. The authors compared the strength of the theories in relation to each other only in a second step. The comparison occurs as a comparison of their quality to generate correct predictions. This means that no theory is falsified or verified. This approach indicates that case studies are able to shed light on the relative strength of specific theories, and that a strictly deductive approach for generating the predictions and the strict tools of statistics for analyz-

ing the congruence between observations and predictions can be combined with a qualitative approach for generating and coding the observations. Nevertheless, it is quite telling to see that the authors have to reflect upon how to improve the “interpretive” part of the research technique at the end of their study. They realize that in comparison to the sophisticated statistical analysis of the coded data, the production of this data is not as methodologically reflected. What would be necessary is much more reflection about how the specific framing of the questions determines the results (Wilson and Woodside 1999: 219–20). Another, and quite different, example of congruence analysis is Allison and Zelikow’s book on the Cuban Missile Crisis (Allison and Zelikow 1999). The authors differentiate three theoretical approaches for decisionmaking in international politics: the rational actor model; the organizational behavior paradigm; and the governmental politics model. For every approach they discuss the relevant theoretical literature and formulate a “paradigm” which includes (a) the basic unit of analysis, (b) “organizing concepts” (including micro-foundations like the rational choice theory for the first model), (c) “dominant inference pattern,” (d) “general propositions” and (e) the typical evidence which is used within such a paradigm. After each theoretical chapter follows a chapter in which Allison and Zelikow tell the story in a narrative form guided by the theoretical lens developed in the forgoing chapter. As a result, we get three different “cuts” of the Cuban Missile Crises. With the respective theoretical framework in mind, they focus within their empirical chapters on three research questions (“Why did the Soviet Union decide to place offensive missiles in Cuba?”; “Why did the United States respond to the missile deployment with a blockade?” and “Why did the Soviet Union withdraw the missiles?”). Each narrative reveals different interpretations for these “central puzzles” in the Cuban Missile Crisis (Allison and Zelikow 1999: 379): the rational version focuses on the key interests of the two superpowers behind each decision; the organizational version describes routines and organizational processes that influenced and delayed decisions; and the politics version showed that power and influence struggles within both groups of advisors and subordinates also influenced the course of events. In comparison to Wilson and Woodside, the link between theory and empirical information is less strict and stringent. This can be explained by the fact that the main goal of Allison and Zelikow is not the testing of the (relative) empirical adequacy of theories, but to show the capacity of paradigms II and III to reveal factors of influence that would have not


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been considered by the dominant paradigm I. In the methodological reflection about their findings, they acknowledge that the apparent explanatory contradictions between the three different cuts are a consequence of using explananda that are not very specified (Allison and Zelikow 1999: 388). In other words, a case study which uses rather abstract explananda and rather abstract explanatory approaches is neither aiming to reveal which theory is right and which one is wrong (falsification/verification), nor does it try to reveal their relative explanatory strength (as does the Wilson-Woodside approach). It tries to reveal additional insights generated by complementary paradigmatic lenses, in addition to the hegemonic one. Differences between Causal-Process Tracing (CPT) and Congruence Analysis (CON) Those who advocate an alternative to the co-variational approach in case study research usually present only one single alternative, typically a mix between CPT and CON. One example is Peter Hall (2006). His naming and justification of his approach corresponds with CPT but his description of the procedure comes much closer to CON.15 Since we are proposing that there are two different alternatives, it is necessary to clarify the differences between the two. For our pragmatic purposes, it seems not very productive to explain the affinities to different ontological and epistemological backgrounds. Instead, it is quite helpful to characterize CPT as a case-centered and CON as a theory-centered approach (whereas COV is a variable-centered approach). The different focal points have important consequences for the kind of observations which are most useful and for the locus of coherence. The latter should be reflected in the way the findings are presented. Furthermore, it is strongly connected with the direction to which we want to generalize the findings – an issue which we address in the next section. (1) The Most Useful Observations.–––A major difference between CPT and CON shows up when we reflect on which kind of observations are most useful or important for drawing inferences. For CPT, an observation The same is true for all the non-political science examples which Brady, Collier and Seawright (2006: 360–65) use to defend their concept of “causal process observations.” No one of these examples puts emphasis on the temporal unfolding of a causal process. From our perspective, they fit much better into our understandings of observations within a congruence analysis. This is not the case, though, for the political science examples which Brady, Collier and Seawright (2006: 365–67) use in their defense. 15

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is most helpful when it leaves no doubt that an (unobservable) mechanism really leads to an effect. A large number and spatial-temporal contiguity of details help to reduce those doubts. We use the term smoking gun observation for observations providing a high level of certainty for a specific explanation of a specific outcome in the case under study.16 The observation of a smoking gun in the hands of a suspect is only convincing as evidence if it is embedded in a full “picture” with many and diverse observations connected with each other through spatial and temporal contiguity. If we want to be sure that the victim was shot by one specific person, but cannot observe the flying bullet (the causal mechanism), the observation of a smoking gun in the hands of the suspect is only a strong piece of evidence if we can also observe that both persons were present at the same place at the same time, that the gun was directed at person who died and that person fell within seconds after we saw the smoke of the gun, and/or heard its explosion. Such closely-knit webs of observational evidence are prototypical observations for CPT if they include a temporal dimension (which means we need not only one picture of the murder scene but at least two). Contrary to smoking gun observations, which are strong in respect to the certainty with which we can draw causal conclusions,17 the most important observations for CON are those discriminating between two rival theories.18 This is the case if the one observation is at the same time evidence Please note that we depart from Van Evera’s use of the term. For us, smoking gun observations provide both: uniqueness (the observation fits only to one single theoretical explanation) and certitude. Van Evera (1997: 31–32) uses the term certitude in the sense of a necessary condition. For us, certitude or certainty refers only to the inferential leap for the case under study and should not be confounded with considerations about the certainty with which we can generalize the relevance of a causal factor beyond the case under study. 16

Inferences drawn from smoking gun observations are neither based on a counterfactual thought experiment (not even implicitly, because the convincing power of this kind of observation does not rely on the thought “the victim would not have died if the murder did not shoot him”), nor are they “tested” against rival explanations. If we see the full scene in which somebody shoots another person, we do not check anymore whether the victim died because of other reasons (e.g. because of cancer or a stroke). We might check whether the deadly shot came from another gun. Nevertheless, this check would be part of getting a “full” picture of the scene (focussing on the fit between the deadly bullet and gun) and not be part of a test of rival theories. 17

Nevertheless, such a “differential” quality of an observation is only valued within a positivist approach to CON. A consequent post-positivist approach would value the exact opposite: a concrete observation, which can be equally well interpreted as a confirmation of one abstract concept and the other (rival) concept. It would be valuable because it confirms the main massage of such an approach – that the meanings we attach to observations depend 18


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for the correctness of one theory and evidence for the incorrectness of another theory (Hall 2006: 27). For example, if an actor explicitly reflects on her motivation for an action by stating that she did not use an opportunity for reaping individual gains because she felt that was morally unacceptable, this statement both provides evidence against a rational-utilitarian, and for a normative-value-based explanatory theory. Of course, other observations are necessary to corroborate the accuracy of such a statement. Indeed, a CPT approach would invest in finding more details substantiating the actor’s statement as an accurate description of her motivation or identifying whether it is just an ex-post legitimization/rationalization of her activities. Within a CPT approach, this statement would be just one of many observations from which the researcher finally draws inferences about the causal process. Within a CON approach, it would be one of the most valuable observations because many observations do not have such a discriminatory power. The latter would be the case, for example, if the statement of the actor had referred to moral values without explicitly mentioning a deliberate choice against her interests. In this case, a defender of the rational-utilitarian theory could try to argue that moral values were part of her preference function and therefore her statement cannot be seen as evidence against this theoretical approach. (2) Coherent/Complete Cases versus Coherent/Consistent Theories.––– Advocates of CPT and CON are convinced that case studies can overcome the “degree of freedom”-problem because of the many observations within one case. Both refer to the famous article of Campbell (1975), in which he corrected “some of my own prior excesses in describing the case study approach [in a co-variational manner, JB/TB]” (Campbell according to George and Bennett 2005: 29). “In a case study done by an alert social scientist who has thorough local acquaintance, the theory he uses to explain the focal differences also generates predictions or expectations on dozens of other aspects of the culture, and he does not retain the theory unless most of these are also confirmed. In some sense, he has tested the theory with degrees of freedom coming from the multiple implications of any one theory” (Campbell 1975: 181–82). The fact that Campbell has connected the “alertness” of a social scientist to his “local acquaintance” (and not with acquaintance to a plurality of theories) shows that right from the beginning, methodologists which tried to specify an alternative to the co-variational template mixed up the logics of CPT and CON. This also to the most part on our interpretative frameworks.

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becomes evident when we look at how George and Bennett (2005: 29–30) draw conclusions from Campbell’s insight for CPT: “We would go even further than Campbell on this issue. While Campbell states that ‘most’ predictions or expectations a theory makes regarding a case must be confirmed in order for the theory to be retained, we would distinguish retaining a theory that has general utility in many cases from retaining a historical explanation of a particular case. A satisfactory historical explanation of a particular case needs to address and explain each of the significant steps in the sequence that led to the outcome of that case. If even one step in the hypothesized casual process in a particular case is not as predicted, then the historical explanation needs to be modified […]. It is the insistence on providing a continuous and theoretically based historical explanation of a case, in which each significant step towards the outcome is explained by reference to a theory that makes process-tracing a powerful method of inference.”

This statement is puzzling since the authors equate process-tracing with historical explanation, although they stress in most parts of their book that CPT case studies are aiming at contingent generalizations beyond the specific case under investigation. We would argue that “continuity” and “theory-based” are two quite different criteria for specifying the needed observations in a case study. Furthermore, in real-world case study research, one of them has to be prioritized. Since CPT aims to reveal the density of links between causal factors (either by going down towards a lower level of analysis or by searching for deterministic links in causal chains and causal conjunctions), it seems logical that for CPT it is important to cover every significant step and every significant context factor of the process leading towards the outcome (without being able to invest a lot of theoretical reflections on every step). In contrast, the main goal of CON is inference towards broad, abstract theories. Here, it seems logical that the emphasis is on intensive reflection of the link between every significant observation and one or more abstract concepts (without necessarily covering every step of the process). We sum up by stressing that CON is theory-centered in contrast to the case-centered CPT. This means that a CON approach seeks cohesion and consistency on the level of the abstract concept and not on the level of the empirical case. CON aligns all observations, which can be used to draw (confirming and disconfirming) inferences to a specific theory and therefore presents the findings as different “cuts” of the case. In contrast, CPT tries to align all events, processes and mechanisms in a closely connected (spatio-)temporal picture – thereby fully accepting a mix of factors and


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mechanisms which have connotations to quite different theories. It is therefore most adequate to present the findings of CPT first in a narrative style. Different Understandings and Directions of Generalization Just as the different types of case study approaches are based on different ways to draw valid (causal) inferences from observations to unobservables for the cases under investigation, they aim towards different directions in their effort to draw generalizing conclusions beyond the observed cases. These different directions of generalization have an important impact on the adequate criteria and strategies for case and/or theory selection (see Table 2). Generalization within a Co-Variational Approach (Statistical Generalization) There is a close affinity between advising co-variation as the major approach to draw causal inferences and the aim to draw general conclusions from the study of one or a few cases to a wider population of similar cases. Gerring defines a case study as “the intensive study of a single case where the purpose of the study is – at least in part – to shed light on a larger class of cases (a population)” (Gerring 2007a: 20). Indeed, for Gerring, the very notion of “case study” is connected by definition to this goal of generalization across a population. “This book has understood case studies as a method for generalizing across populations. The population may be small or large, but the analysis is synecdochic. It infers a larger whole from a smaller part” (Gerring 2007a: 187). Therefore, he calls all studies not having this goal “single-outcome studies”, and devotes an epilogue to this kind of study. Gerring’s book is a major step forward in clarifying the necessary preconditions for using the COV approach to draw further conclusions from one or a few observed cases to the wider population of unobserved cases. This goal has two implications: First, the researcher must reflect on the boundary of the population to which the investigated proposition should apply. Second, she must try to find out the status and location of the observed case(s) within the wider population. (1) Specifying and Justifying the Boundary of the Population.–––Gerring argues that “[…] it is absolutely crucial that case study writers be as

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Table 2: Different understandings and directions of generalization. Understanding

Direction of Generalization

Statistical Generalization

Drawing conclusions about the Specification and justifying the strength of a causal variable from boundary of the wider population. specific cases to the wider populaSelecting cases according to their tion of similar cases. statistical location within the wider population (e.g. typical, diverse).

Contingent Generalization

Drawing conclusions from identified causal configuration (with evidence for the links and interactions between causal factors) to the wider set of potentially possible configurations.

Specification and justification of the wider set of potential causal configurations.

And/Or

And/Or

Drawing conclusions from traces of causal mechanisms for the accuracy and consistency of multilevel causal models.

Specification and justification of multi-level models with generic causal mechanisms.

Drawing conclusions from the congruence between theoretical expectations and empirical observations in the empirical case(s) to the relevance/relative strength of theories within the broader scientific discourse.

Specification and justification of the range of theories which are applied.

Abstraction

Preconditions and Consequences for Case/Theory Selection

Selecting cases according to their preliminary classification into theoretically interesting types of causal configurations.

Selecting cases according to “accessibility” and “familiarity” in order to be able to have a deep look into the levels where causal mechanisms reside

Selecting cases according to their “likeliness” for the dominant theory.

clear as possible about which of their propositions are intended to describe the case under intensive investigation, and which are intended to apply to a broader set of cases. Each inference must have a clear breadth, domain, scope, or population […]. If at the end of a study, the population of the primary inference remains ambiguous, so does the hypothesis. It is not falsifiable” (2007a: 80). Since most methodologically reflective case-study


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researchers try to hide from criticism by formulating rather narrow populations to which they want to generalize, Gerring (2007a: 81–83) points to the costs of doing so. The following arguments are convincing within the COV approach: 1) wider inferences enhance the possibility for falsification, 2) the specification of the population is necessary to be able to draw any causal inferences, and 3) “the scope of an inference usually correlates directly with its theoretical significance” (Gerring 2007a: 82). Furthermore, Gerring is certainly correct in arguing that “all populations must not only be specified, but also justified,” and narrow specifications can be equally arbitrary than wider specifications. Finally, his suggestion to differentiate between the manifest scope – “a limited set of cases that a given proposition must cover if it is to make any sense at all” – and the potential scope – “the larger population of cases that may be included in the circumference of the inference” [emphasis in the original, JB/TB] (Gerring 2007a: 83) is very interesting because it is a step away from the adherence to clear-cut and single boundaries (of populations). (2) Selecting Cases According to Their Location within the Population.–––There has been a long tradition among researchers and methodologists who adhere to the co-variational template for drawing causal inferences to advocate the selection of a few cases on the basis of similarities (in respect to background conditions and/or in respect to major alternative explanatory factors), in order to be able to isolate the consequences of differences among the key explanatory variables (Przeworski and Teune 1970, Lijphart 1975). Implicitly or explicitly, it was assumed that the findings could be generalized to all cases exhibiting the same basic similarities as the selected cases. Nevertheless, little attention was given to the boundary of the entire population and to the specific location of the selected cases (in respect to their values on the dependent and/or on the independent variable of interest) within the population of “comparable” cases. In the largest chapter of his book, Gerring (together with Jason Seawright) addresses the different logics of case selection in an unprecedented way and makes clear which techniques of case-selection are conductive for the goal of generalizing towards the wider population of similar cases. At the beginning of this chapter he states: “[…T]he main point of this chapter is to show how case-selection procedures rest, at least implicitly, upon an analysis of a larger population of potential cases. The case(s) identified for intensive study is chosen from a population, and the reasons for this choice hinge upon the way in which it is situated within that population. […] It follows that case-selection procedures in case study research may build

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upon prior cross-case analysis and depend, on the vary least, upon certain assumptions about the broader population.” (Gerring 2007a: 89). Gerring and Seawright discuss the following nine case study types: typical, diverse, extreme, deviant, influential, crucial, pathway, most-similar, and most-different. It is fully consistent with the goal of generalizing towards the wider population that the techniques of selecting cases on the basis of prior statistical analysis are clearly preferred. They effectively dismiss the logics of selecting most-different or “crucial” cases (for the latter see also Gerring 2007b), and provide an entirely new approach to selecting “most-similar” cases by concentrating on the possibilities of statistic “matching” techniques (Gerring 2007a: 134–39). Overall, since the techniques of choosing cases fully in line with the goal to generalize towards a wider population rely heavily on statistics, it seems adequate to call this logic of generalization “statistical generalization” (Yin 2003: 10). Generalization within a Causal Process Tracing Approach (Contingent Generalization) According to its proponents, CPT is aiming not only to reveal the specifics of a single historical event, but also to draw generalizing conclusions beyond the case under investigation (e.g. George and Bennett 2005: 17). In this they go beyond those case study methodologists who argue that case studies should concentrate on the unique features of a case and who believe only in an extra-scientific “natural generalization” through social learning (e.g. Stake 1995). (1) Contingent Generalization.–––In accordance with our attempt to differentiate between causal mechanisms and various forms of causal configurations as the ontological concepts a CPT approach is aiming to reveal, we differentiate similar forms of contingency in order to clarify what “contingent generalizations” (George and Bennett 2005: 110, 266) mean. Sandra Mitchell (2002: 183–87) provides an overview of different understandings of contingency, which can be found in biological research. Three main forms of contingency can be differentiated: a) evolutionary or historical contingency, b) multi-component contingency, and c) multi-level contingency. Historical and evolutionary contingency correspond to the asymmetrically deterministic ontologies of causal configurations (necessary and sufficient conditions) because these notions imply that the working of causal factors are dependent on (time or space-)specific contexts or


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on prior conditions in time. Multi-component contingency is the result of the fact that the interactions of causal factors are not based on simple rules, like additivity, but on complex feedback mechanisms. This corresponds exactly to our first understanding of causal configuration. Finally, multilevel contingency fits well into our understanding of causal mechanisms because it points to the fact that the working of causal factors can be dependent on their embedding in multi-level structures. George and Bennett’s understanding of contingency is basically restricted to historical and evolutionary contingency. They stress the relevance of context conditions, path dependencies, temporal sequences and argue that general factors only work under specific circumstances, which are only the same among a very limited range of cases. This kind of reasoning provides the impression that CPT does not really strive for wider generalizations but for narrower specifications. Nevertheless, the chapter on typological theorizing opens up a different understanding of CPT’s direction of drawing conclusions beyond the cases under study not confined to the one-dimensional distinctions between “splitters (particularizers)” and “lumpers (generalizers)” (Gerring 2007a: 77). If case studies with a CPT approach are combined with typological theorizing, we can formulate that CPT tries to generalize from the causal configuration(s) within the cases under investigation towards a theoretical typology (George and Bennett 2005: 233–60). In typological theorizing, it is neither the individual case nor the individual variable that form the conceptual core of the explanatory approach, but configurations of variables and (sub-)classes of cases which inhibit these configurations. Generalizing within such an approach means to draw inferences from specific causal configurations that are identified within one or a few cases towards the entire set of potential causal configurations. This can be done inductively by developing such a set of potential configurations and deductively by testing which configurations actually exist, and whether the variables in the configuration are really densely connected. Such a form of generalization, which we could also call typological or configurative generalization, is not aiming to draw generalizing inferences in respect to the strength of a causal configuration within a specified population of cases (as in statistical generalization). We escape the confines of one-dimensional thinking about generalization also if we conceptualize the direction to which CPT draws conclusions beyond the cases under study in a vertical way, towards deeper levels of analysis. Case studies can be used to check whether the sophisticated multi-level models that have already been formalized within rational choice

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theory really capture the processes of preference generation and decisionmaking, and the processes of aggregation of individual actions (including the consequences predicted by game theory, for example). They can also be used to bolster modified models (including prospect theory, for example) or to formulate similar sophisticated models and mechanisms for sociological and cultural micro-foundations (some of the very first steps to do this for the cultural concept of “performance” can be found in Blatter 2008). (2) Selection of Cases.–––The criteria and methods for selecting cases in a CPT approach are quite different from those proposed by COV. Whether we want to trace the implications of causal configurations or of causal mechanisms, the crucial precondition is that we are able to take a deep look into the black box of the causal process. This means that access to sources and actors is indispensable. Familiarity with the historical and cultural context is a further necessity. We would like to stress the fact that cases selected according to these criteria within a CPT approach should not just be tolerated as a matter of practice in real-world research, but they should be seen as selected according to methodological rules! Beyond these two criteria being essential for generating valid inferences, there are other criteria resulting from the aim to draw conclusions beyond the cases under study. For contingent generalization in the sense of configurational generalization, it is adequate to start from a typological theory including a full set of potential configurations. Ideally, the next step would be a Fuzzy Set Qualitative Comparative Analysis with a medium number of cases in order to find out which configurations are empirically relevant (for an extended argument that FS QCA is the appropriate first empirical step/research design if we want to do CPT in the case study proper see Blatter and Blume 2008). In a final step, we would study those cases in detail that represent theoretically or empirically relevant configurations in order to find out whether dense links exist among the causal factors and/or whether other factors not included in the typological theory have played an important role. If we strive to draw conclusions towards causal mechanisms, the ideal-type logic of case selection is the same as in congruence analysis (see next section). There is one more consideration on case selection which is in line with the logic of CPT: since contingent generalization is not primarily concerned with drawing generalizing conclusions to a wider population, it is only consequent to focus on cases which have a large real-world impact on their own. Insights gained from these cases are already quite relevant


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for policy-making even if they cannot be generalized towards a population of similar cases. Generalization within a Congruence Analysis Approach (Abstraction) We would like to remind the reader that within a co-variational (COV) approach generalization is understood to draw inferences from the “specific” to the “universal”. “Specific” refers to co-variations of variables identified across a few cases or within a case over time, “universal” to a valid cover-law-like causal proposition within the adequate population of similar cases. Against this background, we propose that we should understand generalization within a CON approach as an attempt to draw inferences from the “concrete” to the “abstract”. “Concrete” is defined as the (non)congruence between predictions deduced from theories and empirical observations within one or a few cases, whereas “abstract” refers to the concepts which are the elements of a theory or a paradigm. Theories and paradigms provide coherent interpretative frameworks for the understanding and explanation of events and outcomes within a (scientific) discourse. The domain of general theories and analytical frameworks (like Neo-Realism and Liberal Intergovernmentalism in IR or the “Institutional Analysis and Development Framework” and the “Advocacy-Coalition Framework” in policy analysis) is not restricted to a specific population of cases.19 (1) Some Clarifications of Terms.–––The understanding of generalization in COV approaches is horizontally oriented, whereas generalization in the context of CON points to what Sartori calls “the vertical organization of knowledge” (Sartori 1984: 44). In order to get a better understanding of the latter kind of generalization, it is necessary to clarify some terms. Yin (2003: 10) expresses a basic assumption for a CON approach when he argues that “case studies, like experiments, are generalizable to theoretical propositions and not to populations or universes. In this sense, the case study […] does not represent a “sample”, and in doing a case study, your goal will be to expand and generalize theories (analytic generalization) and not to enumerate frequencies (statistical generalization)”. Nevertheless, this description has two problems: First, it does not distinguish Dul and Hak (2008: 46–47) illustrate very well the difference between the population of cases and the domain of a theory. The domain of a theory represents the universe of all possible instances of the object of study to which the theory applies and is principally unlimited. This means that neither a small-n, nor a large-n-study can be representative for the domain of a theory. 19

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between “drawing inferences”, as described in section two of this paper (drawing conclusions about the capability of an abstract concept to explain/understand a specific empirical case through congruence analysis), and “generalization” as drawing conclusions from these empirical findings for the relative strength or relevance of a theory within a broader set of theories and paradigms. Second, the term “analytic generalization” is rather misleading, both in its connotative and denotative implications. It seems to refer to the “level of analysis”, which is not the same as the “level of abstraction”. Furthermore, the term “analytic” is closely connected to a strictly positivist concept of social science. Since a CON approach puts heavy emphasis on interpretation as the main technique for drawing inferences, it is also compatible with constructivist epistemologies. The next step is to clarify the difference between generalization and abstraction. Confusingly, Sartori has equated the two terms when he developed his famous “ladder of abstraction” (1970: 1040–46, 1984: 44–46). Nevertheless, Collier and Mahon (1993: 846 and footnote 5) have realized the confusing naming introduced by Sartori and have renamed his ladder much more appropriately as a “ladder of generality”. This is an important insight because it is very doubtful whether Sartori’s ladder ever reached an “abstract” level. This is because his hierarchical classification scheme just covered “observational terms” and not “theoretical terms”. Satori himself pointed out (but did not take into account later on) that theoretical terms are defined by their “system meaning” (Sartori 1970: 1041) and not by reference to an observed reality.20 A CON approach is based on the assumption that the inferential leap between concrete empirical observations and abstract theoretical concepts cannot be done in a mechanical or standardized way (e.g. by assigning exante a fixed set of observable properties/attributes to every concept or by delineating exclusionary categories in which every observation is exclusively connected to one abstract concept). Instead, the meaning of an abstract concept is delineated by embedding it into a full-fledged theoretical Sartori separates three levels of abstraction: (1) “Universal conceptualizations” are on the highest level, characterized by a maximal extension and a minimal intension and defined by negation; (2) “General conceptualizations and taxonomies” on the medium level balance extension and intension and are “defined by analysis, i.e. per genus et differentium”; and (3) “configurative conceptualizations” on the lowest level are defined contextually, with maximal intension and minimal extension (Sartori 1970: 1044). Especially the fact that Sartori assumes that universal conceptualizations are defined by negation makes it obvious that these are not theoretical terms. 20


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framework (e.g. the concept of “institutions” is usually understood within an economic institutionalist framework as rules which can be sanctioned and which guide the actors strategies but not their preferences) and by contrasting it to meanings within other frameworks (e.g. the understanding of institutions within a sociological institutionalist framework). Not only full-fledged concepts, but also specific properties of concepts can have different meanings depending on their theoretical embedding. In order to come to a conclusion, researchers usually rely not on single observations but on clusters of observations forming a meaningful gestalt in the light of a theoretical framework (Davis 2005: 42–57, Blatter, Janning and Wagemann 2007: 147–48). Ideal-typically – and in contrast to CPT, where detailed knowledge of the empirical cases is essential – the main precondition for drawing an abstract conclusion from empirical observation is a profound knowledge of all specified variants of a theoretical discourse within a paradigm (research program) and beyond. The empirical findings of case studies are used in theoretical discourses to indicate the relevance of a specific theoretical framework or to modify a theoretical framework. (2) Specifying and Justifying the Breadth of Abstract Concepts.–––In a similar way as researchers applying a COV approach have to reflect on the range of cases (the population) to which the insights from the study of one or a few specific cases can be transferred, researchers using a CON approach must reflect on the range and breadth of the abstract concepts (theories and paradigms) to which they link their concrete observations or which they use to generate concrete predictions. Usually something like this is done in the “state of the art” and in the “model building”-section of a research proposal/report, but in most case studies there is no explicit reflection on the boundaries of the scientific field from which theories are drawn. Should we stick to the theoretical concepts which have been already used in the field of research – or should we introduce more abstract concepts, which have been developed and beyond the specific field of investigation? In a similar way as Gerring argues for an explicit specification and justification of the population of cases to which a causal proposition is generalized, it is necessary within a CON approach to reflect very explicitly on the range of theories used for CON. Furthermore, transferring Gerring’s arguments on wider generalizations within a COV approach to the CON approach, we conclude that researchers should strive to use more abstract concepts because they have broader implications within the overall scientific discourse. When more abstract concepts are applied, it is often

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more difficult to connect specific observations to one, and just one, specific abstract concept (this is especially problematic for those trying to find the “better” or “false/correct” theory). Nevertheless, it is exactly the strength of case studies that the low number of cases enables the researcher to invest time and effort for intensive reflection on the implications of specific observations for a broad range of abstract concepts or for a quite abstract concept. Case studies are an especially fruitful tool for theoretical innovation because a broad set of theories and/or quite abstract theories can be connected to empirical information. A broad range of theoretical lenses does not only lead to a more comprehensive understanding of a specific case. The diversity of observations which can be collected, and the intensity with which a researcher can reflect on the relationship between concrete observations and abstract concepts (a character of case studies we call thickness) make it possible to use the empirical findings for drawing conclusions towards a comprehensive and a diverse range of theories. It is the plurality and diversity of observations that a researcher obtains within one or a few cases, which allow her to reflect on the adequacy and relevance of a broad range of theories. This is especially helpful in a research field where a differentiated set of specified theories already exists. The intensive reflections on the theoretical meanings of empirical observations make it possible to draw generalizing inferences on different levels of abstraction. This enables the researcher to explicitly reflect on the question whether the findings should be used to modify or expand existing theories or whether it is evidence providing leverage for a paradigm change. But we also want to argue for applying existing theoretical frameworks for new fields (in order not to invent the wheel one more time). Too many case study researchers try to defend a research approach without a theoretical framework by stating that there is no established theory. Maybe we cannot be sure which theoretical framework is the most adequate at the beginning of a research project, but we surely should try to translate our empirical findings into abstract concepts which can be connected to general theoretical frameworks. Since adherents of CON take the filtering and framing consequences of (hegemonic) theories seriously, they do not dismiss the use of case-studies for the important battles between major theoretical frameworks in the social sciences – as many proponents of case-study research (mainly adherents of CPT) more or less explicitly do. (3) Example.–––An example illustrating these capabilities of case studies can be found in Blatter (2008). He seeks to find out why the transna-


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tional Lake Constance had become a frontrunner in environmental regulation, which not only preceded national legislation but stimulated similar regulations on the EU level. Since the field of international water regulation has been the main field of Regime Theory, and since within Regime Theory approaches there is a very sophisticated and differentiated set of explanatory approaches, Blatter applies the whole set of these explanatory approaches in a first attempt to solve the puzzle. First, he turns to rationalist approaches and discusses the adequacy of four specifications: a “problem-structuralist” approach, a “situation structuralist” account, a “multi-level game” approach and a “realist” conceptualization. There was little evidence for the adequacy of any of these approaches: All of their stipulated necessary preconditions for international environmental regimes were not given. This conclusion was drawn partly by looking at the values of the independent variables which take centre stage within the specific explanatory approaches (e.g. a hegemonic power in realism). The core argument against the adequacy of rationalist accounts was that the most basic assumption of all these approaches – that there are functional interdependencies among riparian states – was indeed not given in the specific sector of regulation on which the investigation focuses (the regulation of motor-boats which was the most politicized issue at Lake Constance). After the discussion of four rationalist theories, Blatter turns to three socialconstructivist approaches for explaining environmental regime formation: informational, cognitive and normative approaches. He discovers that the expectations drawn from these approaches were more congruent with the empirical observations. Especially the cognitive approach drawing on the existence of “epistemic communities” provides considerable leverage for explaining the fact that the issue was placed and kept on the cross-border political agenda. In comparison to the “advocacy coalition” approach, however, which is an approach from outside the domain of International Regime Theory, the “epistemic community” approach provided a far less complete picture of the reality at Lake Constance. The congruence between the expectations deduced from the advocacy coalition approach and the observed processes of group and belief formation at Lake Constance were quite high. Nevertheless, also this approach could not really explain when and why riparian states actually agreed on a binding and very demanding standard. In consequence, Blatter develops an alternative explanatory approach. Evidence shows that it was neither the environmental discourse nor the policy-field specific institutions, but the search for recognition and profile of general/not water-specific political institutions in times of

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“Euregio” formation that provided the decisive momentum for forging an agreement. He generalizes this empirical finding by connecting it to the theoretical concept of “performance”. This concept is drawn from cultural sociology and stresses the role of performance in order to receive attention and recognition. The alternative account is not only based on a different micro-foundation but challenges the very core of Regime Theory – the assumption that explanations for international regulations can be found within specific policy-domains. In consequence, Blatter reflects on the implications of this finding for Regime Theory and its status as the dominant framework for analyzing international (environmental) regulation. One conclusion could be that his findings point to an additional causal mechanism complementing the other approaches within Regime Theory. Another conclusion would be that the findings undermine the adequacy of the functionalist (in the sense of policy-oriented) paradigmatic framework, which underlies Regime Theory. In the context of this methodological reflection, it is important to note that the second conclusion draws generalizations towards a higher level of abstraction in comparison to the first one because it includes not only an addition to established approaches within a research program, but questions the core assumption of this research program and points to other promising theoretical frameworks beyond this program. (4) The Understanding and Selection of “Crucial Cases”.–––The selection of cases within a CON approach is theory-driven, and not driven by considerations about the overall set of cases (the population) as in COV or the overall set of configurations (the typological theory) as in CPT. For those who see it as the goal of empirical research to test the empirical validity of a theory (in the sense of verification/falsification), or at least to compare the relative strength of a theory to provide correct predictions, the cases must be selected on the basis of prior expectations about their relationship to theories. This also holds for those who want to show the capacity of new paradigms to create new and important understandings beyond the understandings created by established paradigms. CON exhibits therefore a strong affinity to selecting theoretically “crucial cases” (Eckstein 1975). An understanding of crucial cases which is most appropriate for CON reflects on the status of specific theories within a (scientific) discourse.21 For the selection of cases, it is important to identify a hegemonic This understanding of a crucial case is different from the understandings of Eckstein and Gerring. Only if we think that we can falsify theories with case studies the difference between “least-likely cases” and “most-likely cases” is important. This is because least-likely cases can only be used for confirming a theory. In order to do this, the researcher must be 21


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or dominant theory within a field of research and start then to look for “least-likely cases” and “most-likely cases” in respect to this dominant theory. This means that CON applies a Bayesian approach on the level of the theoretical discourse and not for adjusting the explanatory model for a specific case. We start by ordering the theories we apply according to their status within the scientific discourse which means that we (implicitly) rank them according to their probability to explain cases in a specific field of inquiry. Findings from case studies which undermine hegemonic theories usually have a stronger impact than findings which undermine peripheral theories or which bolster dominant theories. An important example for selecting a “least-likely case” is Lijphart’s famous study, “The Politics of Accommodation” (1975 [1968]), “that broke the pluralist camel’s back” (King, Keohane and Verba 1994: 186).22 In this study, Lijphart undermined the hegemony of pluralism in the field of interest mediation in democracies by showing that the Netherlands exhibited an especially stable democracy although a central precondition for stable democracies, according to the pluralist theory (“cross-cutting cleavages”), could not be found. It is important to realize that the Netherlands became a crucial case because Lijphart developed inductively an alternative theory to pluralism (consociationalism), and started a long research program based on the two alternatives. An important example of selecting a “most-likely case” is Malinowski’s study “Crime and Custom in Savage Society” (1966 [1926]). He seeks to test whether the common assumption among anthropologists (the obedience to norms is spontaneous and automatic) is valid, and also developed an alternative explanation for norm following out of his empirical research in an inductive way (cf. Eckstein 1975: 118–19). However, the alternative theory does not have to be developed inductively but can also be introduced deductively, as the example of Allison and Zelikow (1999) shows. From a perspective taking the status of theories within the broader scholable to show that all other theories are not adequate for explaining this case. In contrast, “most-likely cases” are used to disconfirm a theory, and therefore, this theory must be deterministic (Gerring 2007b). The falsificatory terms used in KKV’s account are rather misleading. Pluralism was certainly not falsified by Lijphart’s study and still plays a role in the theory of interest formation and mediation. It is much more adequate to think in terms of “hegemony” or “monopoly”. Pluralism lost its uncontested position in deducing a precondition of stable democracies; or in other words: “cross-cutting cleavages” lost its status as a necessary condition for stable democracies. 22

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arly discourse into account, a case is crucial if it provides strong evidence undermining a dominant theory and supporting an alternative theory. The Thickness of Case Studies and its Potential to Draw Wide, Deep, Dense or Broad Conclusions beyond the Studied Cases (Generalizations) Our investigation into the different understandings and directions of generalization within the different approaches to case study research makes it possible to overcome the common dichotomy used to describe the virtues of case study research (depth) in comparison to large-N research (breadth). It also indicates that the “commonsensical extensity/intensity trade-off” (Gerring 2007a: 48) is only correct when we apply a specific understanding of generalization, which admittedly is the most commonsensical understanding. The traditional co-variational approach equates “breadth” with the number of cases to which a proposition is generalized and argues that propositions striving for breadth are in greater need for cross-case comparison than propositions restricted to a few cases (small scope). “Depth” is equated with “detail, richness, completeness, wholeness, or the degree of variance on an outcome that is accounted for by an explanation”. Based on such an understanding of the terms, Gerring concludes his reflection about the scope of propositions with the following statement: “All we can safely conclude is that researchers invariably face a choice between knowing more about less, or less about more” (Gerring 2007a: 49). If we really want to appreciate the virtues of case studies, we need a broader set of terms and different definitions. For us, the central characteristics of case studies are best captured in the term thickness. Thickness refers to the number and diversity of observations which are conducted within a case, and the intensity with which the researcher reflects on the relationship between the empirical observation and the theoretical references.23 A thin study is a study where only one observation is conducted in respect to the outcome and one observation in respect to every causal factor, and where observations are taken at “face-value” as direct evidence for an abstract concept. By using thickness for describing the general characteristics of case studies, we are able to apply the term depth in a different and more specific way than Gerring. We see depth not as a generic characteristic of cases The second aspect of our definition makes sure that our understanding of the term thick is closer to Geertz (1973) than Gerring’s understanding (Gerring 2007a: 49, footnote 35). 23


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studies, but as one possible aim of doing case studies. We can get deeper insights when we identify empirical evidence of causal mechanisms on a lower level of analysis through the technique of causal process tracing. The search for causal mechanisms is an attempt of moving the border “between the observable world and the unobservable ontological level where causal mechanisms reside” (George and Bennett 2005: 143). Furthermore, we are able to distinguish between depth and density. Causal process tracing can also be used in order to reveal dense links (e.g. complex interactions or deterministic pre- or co-conditions) among elements of a causal configuration. We would like to introduce the term width in exchange of breadth when we talk about the horizontal scope of a proposition relating to a population. In this terminology, the COV approach is aiming to draw implications from one or a few cases to the appropriate wider population of cases. We believe that this formulation has a “resonance”24 within the scholarly discourse and within everyday language that is at least as accurate as existing terminology. Furthermore, it leaves the term breadth as a signifier for our third direction of generalization from case study research. From a CON perspective, case studies are especially valuable research designs because the researcher is able to draw conclusions towards a broad set of specific theories and/or towards abstract concepts which have a broader set of connotations (general frameworks). Equipped with such an enlarged set of more specifically defined terms – entailing a starting point and three different directions for drawing generalizing conclusions – it becomes obvious that the necessary efforts to enhance the thickness of case studies produces a trade-off with the goal to generalize over a wider population of cases. However, it does not produce the same trade-off in respect to the depth, density and breadth of the conclusions. On the contrary, it is the thickness of case studies which makes them a valuable tool for getting deeper, denser and broader insights. Summary and Concluding Remarks We set out to delineate three distinct methodological approaches for case study research, and labeled these approaches with reference to the main Gerring (2001: 52) introduces “resonance” as one of several quality criteria for concept formation. 24

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techniques which are employed in order to draw inferences from observations within one or a few cases to unobservables: co-variation, causal process tracing, and congruence analysis. First, we showed that the co-variational template focuses on data-setobservations (indicators for the dependent and the independent variables) and draws conclusions from this kind of observation to the true value of a causal proposition (a covering-law-like statement predicting a specific direction of influence of an independent variable to an dependent variable). Second, we argued that most case studies do not rely only on data-set-observations but much more on causal process observations in order to draw causal inferences. Nevertheless, our threefold approach goes beyond this already established difference. We argue that there is a fundamental difference between those who draw conclusions from causal process observations (and data-set observations) to the “real” (but unobservable) causal mechanisms and configurations in specific cases, and those who draw inferences towards the relevance of internally consistent and abstract concepts (theories and paradigms). In the last part of the paper we argued that all three methodological approaches emphasize the thickness of case studies. Nevertheless, for those who are interested to generalize the findings of case study research to the wider population of cases the trade-off between this basic characteristic of case studies and their prime goal to produce propositions with a wider scope still exists. We argue that there is no such trade-off for those who use “thick description” within case studies in order to draw deeper/denser or broader conclusions. Therefore, it seems to be more promising, first, to see case studies mainly as a research tool striving for deeper/denser and broader insights and, second, to develop methodological tool kits setting up case studies in a way that foster these goals. There is still a long way to go before we can offer similar consistent, clear-cut and comprehensive methodological advice – as Gerring has provided for the co-variational template – for causal-process tracing and congruence analysis. Nevertheless, we think this is a worthwhile endeavor because it would contribute to unfold the real potential of case studies – and not just translate case study research as statistical analysis writ small.


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Auf der Suche nach Kovariation, Kausalität oder Kongruenz? Zur Differenzierung verschiedener Fallstudientypen Wir schlagen in diesem Artikel vor, drei verschiedene Formen von Fallstudien zu differenzieren. Dabei unterscheiden sich die drei Ansätze vor allem in Bezug auf die Art und Weise wie kausale Schlussfolgerungen gezogen werden, sowie in Bezug auf das Verständnis und die Richtung von Generalisierung. Zwei Aspekte werden dabei herausgestellt: Zum einen wird gezeigt, dass es sinnvoll ist, Prozess- und Kongruenzanalyse als unterschiedliche Alternativen zum dominierenden, auf Kovariation basierenden Ansatz zu verstehen. Zum anderen führt das grundlegende Charakteristikum von Fallstudien, ihre thickness, nur dann zu einem Dilemma in Bezug auf die Generalisierungsfähigkeit der Erkenntnisse, wenn wir Generalisierung als Übertragung der Erkenntnisse von einzelnen Fällen auf eine spezifische Population von „weiteren“ ähnlichen Fällen begreifen. Wenn wir dagegen von den Fällen auf tiefer liegende oder enge Verbindungen zwischen kausalen Faktoren oder auf die Relevanz von einzelnen Theorie in einer breiten Palette von theoretischen Ansätzen schließen wollen, dann stellt die Dichte von Fallstudien kein Dilemma, sondern eine sehr hilfreiche Grundlage dar.


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A la recherche de la co-variation, des liens causaux ou de la congruence? Une conceptualisation plurale des études de cas Dans cet article nous proposons de distinguer trois différents types d’études de cas : la co-variation, l’analyse du procès, et l’analyse de la congruence. Notre but principal est de saisir les différents modes de l’inférence causale des trois approches, ainsi que leurs différentes directions de généralisation. Deux aspects seront soulignés : premièrement, l’analyse du procès et l’analyse de la congruence constituent deux alternatives à la co-variation bien distinctes. Deuxièmement, la densité, que nous considérons comme une caractéristique principale des études de cas, ne posera problème que dans les cas où l’on tente de généraliser les résultats sur une population ample. Par contre, si nous visons à l’analyse profonde pour atteindre des relations denses ou étroites entre les facteurs causaux identifiés – c’est le but de l’analyse de procès – ou bien à trouver de l’évidence empirique afin de contribuer à un discours théorique large – c’est l’objectif de l’analyse de la congruence –, la densité des études de cas se révèle bien utile.

Joachim Blatter is currently Assistant Professor at the Department of Public Administration of the Erasmus University Rotterdam. From July 1st, 2008 he will hold the chair of Political Science at the University of Lucerne (with a focus on Political Theory). His research fields include transboundary cooperation, international relations of regions, governance theory and metropolitan governance. The results of these projects have been published amongst others in: European Journal of International Relations, Zeitschrift für Internationale Beziehungen, West European Politics, Natural Resources Journal, Journal of Urban Affairs, International Journal of Urban and Regional Research, PVS, SPSR and ÖZP. His current research focuses on transformations of political legitimacy, democratic practices and democratic theory under the conditions of migration, multi-mediatization and multi-level governance. In 2007, he co-authored a textbook on Qualitative Policy Analysis (together with Frank Janning and Claudius Wagemann). Address for correspondence: Seminar of Political Science, University of Lucerne, P.O. Box 7464, CH-6000 Lucerne 7, Switzerland. Email: [email protected].

Till Blume is a PhD Candidate at the Department of Politics and Management of the University of Konstanz and the Collaborative Research Centre 485 “Norm and Symbol”, and is an Associate Member of the “Zukunftskolleg”. His current research focuses on the application of organization theory and social constructivist approaches to United Nations peacekeeping operations, and touches upon conflict research in general. He has regional expertise in the Balkans and in Sub-Saharan Africa, particularly West Africa, and has also worked on the Common Foreign and Security Policy of the EU. Recent articles were published in Internationale Politik.

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Address for correspondence: Department of Politics and Management, University of Konstanz, Universitätsstrasse 10, D-78457 Konstanz, Germany. Tel.: +49 (0)7531 88 4835; Email: [email protected].