Designing Case Studies Explanatory Approaches

The authors explore three ways of conducting causal analysis in case studies. They draw on established practices as well as on recent innovations in case study methodology and integrate these insights into coherent approaches. They highlight the core features of each approach and provide advice on each step of the research process.

Joachim Blatter is Professor of Political Science at the University of Lucerne, Switzerland. He has previously held positions at the Erasmus University Rotterdam, The Netherlands, the University of Konstanz, Germany, Free University Berlin, Germany, and the University of St Gallen, Switzerland. His research interests include transformations of governance, citizenship, and democracy. Markus Haverland is Professor of Political Science, Department of Public Administration, Erasmus University Rotterdam, The Netherlands, and lectures on methodology at the Dutch Graduate School for Public Administration and Political Science (NIG) there. Amongst his previous positions was a Jean Monnet Fellowship at the European University Institute, Florence. His research interests include methodology, European Union policy-making, and the effect of the European Union on its member states.

List of Figures and Tables	p. xiii
List of Abbreviations	p. xv
Preface and Acknowledgements	p. xvi
Relevance and Refinements of Case Studies	p. 1
Case studies as cornerstones for theories and research programs	p. 2
The case for case study research	p. 5
The growing relevance of timing, cognition, and interdependence	p. 5
Perforated boundaries in social reality and the social sciences	p. 6
Building bridges between paradigmatic camps	p. 7
The case for a non-fundamentalist and pluralist epistemology	p. 9
Empiricism/Positivism and Critical Rationalism	p. 9
Constructivism/Conventionalism and Critical Theory	p. 10
Pragmatism/Naturalism and Critical Realism	p. 12
The epistemological 'middle ground': Anti-fundamentalist and pluralistic	p. 13
Case study methodology: A brief history and recent contributions	p. 15
Case studies: Toward a generic and multidimensional definition	p. 18
Observations: Toward an adequate understanding of case studies	p. 20
Three approaches to case study research: An overview	p. 23
Research goals and questions	p. 23
Case and theory selection	p. 24
Data generation and data analysis	p. 26
Generalization	p. 31
Co-Variational Analysis	p. 33
Research goals and research questions	p. 35
Ontological and epistemological foundations and affinities	p. 36
Experimental template and counterfactual concept of causation	p. 37
Experimental control versus control in observational studies	p. 38
Probabilistic versus deterministic causality	p. 38
Autonomous versus configurational causality	p. 41
Selecting cases	p. 41
Criteria for case selection	p. 42
Modes of comparison	p. 44
Cross-sectional comparison	p. 45
Intertemporal comparison	p. 46
Cross-sectional-intertemporal comparison	p. 47
Counterfactual comparison	p. 48
Excursus: The method of agreement and the most different systems design	p. 49
The functions of prior knowledge and theory	p. 50
Specifying the main independent and dependent variable	p. 51
Substantiating the research hypothesis	p. 52
Identifying control variables	p. 54
Drawing causal inferences for the cases under investigation	p. 54
Data set results and conclusions	p. 55
Examples	p. 58
Concluding remarks	p. 61
Measurement and data collection	p. 63
Conceptualization and measurement in large-N versus small-N research	p. 63
Determination of classifications and cut-off points	p. 65
Replicability and measurement error	p. 67
Data triangulation	p. 68
Direction of generalization	p. 68
Presenting findings and conclusions	p. 70
Example of best practice: Zangl's Judicalization Matters!	p. 71
Summary and conclusions	p. 75
Appendix: How to make counterfactual analysis more compelling	p. 76
Causal-Process Tracing	p. 79
Research goals and research questions	p. 84
Starting points and research goals	p. 84
Research goals and functions of causal-process tracing	p. 87
Research questions	p. 88
Ontological and epistemological foundations	p. 90
Contingency	p. 91
Causal conditions and configurations	p. 92
Additive and interactive configurations	p. 93
Causal conjunctions and causal chains	p. 94
Social and causal mechanisms	p. 95
Summary	p. 97
Appendix: Contexts	p. 98
Selecting cases	p. 99
Misleading advice and trade-offs	p. 99
General criteria for selecting cases	p. 102
Specific criteria for selecting cases according to different research goals	p. 102
Collecting empirical information	p. 105
Drawing causal inferences for the case(s) under investigation	p. 106
The added value of causal-process observations	p. 107
Major features of causal-process tracing	p. 109
Empirical fundaments of CPT: Storylines, smoking guns, and confessions	p. 110
Logical foundations of CPT I: Causal chains	p. 119
Logical foundations of CPT II: Process dynamics	p. 121
Examples	p. 123
Brady's Data-Set Observations versus Causal-Process Observations	p. 124
Skocpol's States and Social Revolutions	p. 127
Tannenwald's The Nuclear Taboo	p. 130
Direction of generalization	p. 134
Implicit and explicit generalizations	p. 135
'Possibilistic' generalization	p. 135
Drawing conclusions to the sets of causal conditions and configurations	p. 137
Drawing conclusions to the sets of social and causal mechanisms	p. 139
Presenting findings and conclusions	p. 141
Summary	p. 142
Congruence Analysis	p. 144
Research goals and research questions	p. 148
Research goals	p. 149
Research questions	p. 150
Ontological and epistemological foundations and affinities	p. 152
Illustrating the epistemological foundation of the CON approach	p. 152
Relationships between theories	p. 154
Implications for the congruence analysis approach	p. 160
Selecting theories and cases	p. 167
Selection and specification of theories	p. 169
Selection and specification of cases	p. 175
Crucial cases	p. 176
Formulating expectations and collecting data	p. 178
The specification of propositions	p. 179
Concrete expectations: Predictions	p. 185
The collection of information and production of data	p. 187
Data analysis - The congruence analysis proper	p. 188
The steps of the congruence analysis proper	p. 189
The full set of possible conclusions	p. 189
Examples: Applications of the congruence analysis proper	p. 191
Direction of generalization	p. 197
Theoretical generalization within a competing theories approach	p. 198
Theoretical generalization within a complementary theories approach	p. 200
Presenting findings and conclusions	p. 202
Summary	p. 203
Combining Diverse Research Approaches	p. 205
Combining approaches and designs: Purposes and possibilities	p. 207
Strengthening concept validity of descriptive inference	p. 208
Strengthening or testing the internal validity of causal inference	p. 210
Complementing the range of variables, conditions, mechanisms, and theories	p. 211
Increasing the external validity of causal inferences	p. 211
Combining co-variational analysis and causal-process tracing	p. 212
X-centered combination of COV and CPT	p. 212
Y-centered combination of cross-case comparisons and CPT	p. 216
Combining congruence analysis and causal-process tracing	p. 218
Causal-process tracing as part of a congruence analysis	p. 218
Causal-process tracing as an inductive addition to the deductive congruence analysis	p. 219
Connecting case studies to large-N studies	p. 224
Case studies augmenting large-N studies	p. 224
Case studies preceding large-N studies	p. 229
Connecting case studies to medium-N studies	p. 231
Qualitative Comparative Analysis as a follow-up to case studies	p. 232
Case studies as a follow-up to a Qualitative Comparative Analysis	p. 234
Preconditions for combining different explanatory approaches	p. 236
Final remarks	p. 237
Notes	p. 239
Bibliography	p. 245
Index	p. 256
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Designing Case Studies Explanatory Approaches in Small-N Research

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