Designing case studies : explanatory approaches in small-n research / Joachim Blatter and Markus Haverland.

Format:

Book

Author/Creator:

Blatter, Joachim, 1966-

Contributor:

Haverland, Markus, 1967-

Series:

Research methods series

Language:

English

Subjects (All):

Political science--Research--Methodology.

Political science.

Social sciences--Research--Methodology.

Social sciences.

Case method.

Political science--Research.

Physical Description:

xviii, 262 pages : illustrations ; 23 cm.

Place of Publication:

Houndmills, Basingstoke, Hampshire ; New York, NY : Palgrave Macmillan, 2012.

Summary:

Designing Case Studies explores three different ways of conducting causal analysis in case studies: co-variational analysis, causal-process tracing, and congruence analysis. It is an inclusive account of case study methodology which covers all the major explanatory approaches, and is also the first book to present congruence analysis in detail as a distinct case study approach. Differentiating the three approaches to case study research allows the authors to present each as a coherent and consistent way of drawing causal inferences by studying one or a few cases in-depth. The authors highlight the core features of each approach and provide helpful advice for each step of the research process, including: formulating research questions and goals, selecting theories and cases, data generation, and data analysis. They also show how to draw conclusions beyond the cases under investigation and show how case studies can fruitfully be combined with statistical analysis and Qualitative Comparative Analysis. Book jacket.

Contents:

1 Relevance and Refinements of Case Studies 1

1.1 Case studies as cornerstones for theories and research programs 2

1.2 The case for case study research 5

1.2.1 The growing relevance of timing, cognition, and interdependence 5

1.2.2 Perforated boundaries in social reality and the social sciences 6

1.2.3 Building bridges between paradigmatic camps 7

1.3 The case for a non-fundamentalist and pluralist epistemology 9

1.3.1 Empiricism/Positivism and Critical Rationalism 9

1.3.2 Constructivism/Conventionalism and Critical Theory 10

1.3.3 Pragmatism/Naturalism and Critical Realism 12

1.3.4 The epistemological 'middle ground': Anti-fundamentalist and pluralistic 13

1.4 Case study methodology: A brief history and recent contributions 15

1.5 Case studies: Toward a generic and multidimensional definition 18

1.6 Observations: Toward an adequate understanding of case studies 20

1.7 Three approaches to case study research: An overview 23

1.7.1 Research goals and questions 23

1.7.2 Case and theory selection 24

1.7.3 Data generation and data analysis 26

1.7.4 Generalization 31

2 Co-Variational Analysis 33

2.1 Research goals and research questions 35

2.2 Ontological and epistemological foundations and affinities 36

2.2.1 Experimental template and counterfactual concept of causation 37

2.2.2 Experimental control versus control in observational studies 38

2.2.3 Probabilistic versus deterministic causality 38

2.2.4 Autonomous versus configurational causality 41

2.3 Selecting cases 41

2.3.1 Criteria for case selection 42

2.3.2 Modes of comparison 44

2.3.3 Cross-sectional comparison 45

2.3.4 Intertemporal comparison 46

2.3.5 Cross-sectional-intertemporal comparison 47

2.3.6 Counterfactual comparison 48

2.3.7 Excursus: The method of agreement and the most different systems design 49

2.4 The functions of prior knowledge and theory 50

2.4.1 Specifying the main independent and dependent variable 51

2.4.2 Substantiating the research hypothesis 52

2.4.3 Identifying control variables 54

2.5 Drawing causal inferences for the cases under investigation 54

2.5.1 Data set results and conclusions 55

2.5.2 Examples 58

2.5.3 Concluding remarks 61

2.6 Measurement and data collection 63

2.6.1 Conceptualization and measurement in large-N versus small-N research 63

2.6.2 Determination of classifications and cut-off points 65

2.6.3 Replicability and measurement error 67

2.6.4 Data triangulation 68

2.7 Direction of generalization 68

2.8 Presenting findings and conclusions 70

2.9 Example of best practice: Zangl's Judicalization Matters! 71

2.10 Summary and conclusions 75

2.11 Appendix: How to make counterfactual analysis more compelling 76

3 Causal-Process Tracing 79

3.1 Research goals and research questions 84

3.1.1 Starting points and research goals 84

3.1.2 Research goals and functions of causal-process tracing 87

3.1.3 Research questions 88

3.2 Ontological and epistemological foundations 90

3.2.1 Contingency 91

3.2.2 Causal conditions and configurations 92

3.2.3 Additive and interactive configurations 93

3.2.4 Causal conjunctions and causal chains 94

3.2.5 Social and causal mechanisms 95

3.2.6 Summary 97

3.2.7 Appendix: Contexts 98

3.3 Selecting cases 99

3.3.1 Misleading advice and trade-offs 99

3.3.2 General criteria for selecting cases 102

3.3.3 Specific criteria for selecting cases according to different research goals 102

3.4 Collecting empirical information 105

3.5 Drawing causal inferences for the case(s) under investigation 106

3.5.1 The added value of causal-process observations 107

3.5.2 Major features of causal-process tracing 109

3.5.3 Empirical fundaments of CPT: Storylines, smoking guns, and confessions 110

3.5.4 Logical foundations of CPT I: Causal chains 119

3.5.5 Logical foundations of CPT II: Process dynamics 121

3.6 Examples 123

3.6.1 Brady's Data-Set Observations versus Causal-Process Observations 124

3.6.2 Skocpol's States and Social Revolutions 127

3.6.3 Tannenwald's The Nuclear Taboo 130

3.7 Direction of generalization 134

3.7.1 Implicit and explicit generalizations 135

3.7.2 'Possibilistic' generalization 135

3.7.3 Drawing conclusions to the sets of causal conditions and configurations 137

3.7.4 Drawing conclusions to the sets of social and causal mechanisms 139

3.8 Presenting findings and conclusions 141

3.9 Summary 142

4 Congruence Analysis 144

4.1 Research goals and research questions 148

4.1.1 Research goals 149

4.1.2 Research questions 150

4.2 Ontological and epistemological foundations and affinities 152

4.2.1 Illustrating the epistemological foundation of the CON approach 152

4.2.2 Relationships between theories 154

4.2.3 Implications for the congruence analysis approach 160

4.3 Selecting theories and cases 167

4.3.1 Selection and specification of theories 169

4.3.2 Selection and specification of cases 175

4.3.3 Crucial cases 176

4.4 Formulating expectations and collecting data 178

4.4.1 The specification of propositions 179

4.4.2 Concrete expectations: Predictions 185

4.4.3 The collection of information and production of data 187

4.5 Data analysis - The congruence analysis proper 188

4.5.1 The steps of the congruence analysis proper 189

4.5.2 The full set of possible conclusions 189

4.5.3 Examples: Applications of the congruence analysis proper 191

4.6 Direction of generalization 197

4.6.1 Theoretical generalization within a competing theories approach 198

4.6.2 Theoretical generalization within a complementary theories approach 200

4.7 Presenting findings and conclusions 202

4.8 Summary 203

5 Combining Diverse Research Approaches 205

5.1 Combining approaches and designs: Purposes and possibilities 207

5.1.1 Strengthening concept validity of descriptive inference 208

5.1.2 Strengthening or testing the internal validity of causal inference 210

5.1.3 Complementing the range of variables, conditions, mechanisms, and theories 211

5.1.4 Increasing the external validity of causal inferences 211

5.2 Combining co-variational analysis and causal-process tracing 212

5.2.1 X-centered combination of COV and CPT 212

5.2.2 Y-centered combination of cross-case comparisons and CPT 216

5.3 Combining congruence analysis and causal-process tracing 218

5.3.1 Causal-process tracing as part of a congruence analysis 218

5.3.2 Causal-process tracing as an inductive addition to the deductive congruence analysis 219

5.4 Connecting case studies to large-N studies 224

5.4.1 Case studies augmenting large-N studies 224

5.4.2 Case studies preceding large-N studies 229

5.5 Connecting case studies to medium-N studies 231

5.5.1 Qualitative Comparative Analysis as a follow-up to case studies 232

5.5.2 Case studies as a follow-up to a Qualitative Comparative Analysis 234

5.6 Preconditions for combining different explanatory approaches 236

5.7 Final remarks 237.

Notes:

Includes bibliographical references (pages 245-255) and index.

ISBN:

9780230249691

0230249698

OCLC:

774489697

Online:

Cover image