Designing case studies : explanatory approaches in small-N research /

Designing case studies : explanatory approaches in small-N research /

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Bibliographic Details
Author / Creator:Blatter, Joachim, 1966-
Imprint:Houndmills, Basingstoke, Hampshire : Palgrave Macmillan, 2012, ©2012.
Description:xviii, 262 pages : illustrations ; 23 cm.
Language:English
Series:Research methods series
Research methods series.
Subject:
Political science -- Research -- Methodology.
Social sciences -- Research -- Methodology.
Case method.
Case method.
Political science -- Research -- Methodology.
Social sciences -- Research -- Methodology.
Format: E-Resource Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8863086
Hidden Bibliographic Details
Other authors / contributors:Haverland, Markus (Markus)
ISBN:9780230249691
0230249698
Notes:Includes bibliographical references and index.
Table of Contents:
  • List of Figures and Tables
  • List of Abbreviations
  • Preface and Acknowledgements
  • 1. Relevance and Refinements of Case Studies
  • 1.1. Case studies as cornerstones for theories and research programs
  • 1.2. The case for case study research
  • 1.2.1. The growing relevance of timing, cognition, and interdependence
  • 1.2.2. Perforated boundaries in social reality and the social sciences
  • 1.2.3. Building bridges between paradigmatic camps
  • 1.3. The case for a non-fundamentalist and pluralist epistemology
  • 1.3.1. Empiricism/Positivism and Critical Rationalism
  • 1.3.2. Constructivism/Conventionalism and Critical Theory
  • 1.3.3. Pragmatism/Naturalism and Critical Realism
  • 1.3.4. The epistemological 'middle ground': Anti-fundamentalist and pluralistic
  • 1.4. Case study methodology: A brief history and recent contributions
  • 1.5. Case studies: Toward a generic and multidimensional definition
  • 1.6. Observations: Toward an adequate understanding of case studies
  • 1.7. Three approaches to case study research: An overview
  • 1.7.1. Research goals and questions
  • 1.7.2. Case and theory selection
  • 1.7.3. Data generation and data analysis
  • 1.7.4. Generalization
  • 2. Co-Variational Analysis
  • 2.1. Research goals and research questions
  • 2.2. Ontological and epistemological foundations and affinities
  • 2.2.1. Experimental template and counterfactual concept of causation
  • 2.2.2. Experimental control versus control in observational studies
  • 2.2.3. Probabilistic versus deterministic causality
  • 2.2.4. Autonomous versus configurational causality
  • 2.3. Selecting cases
  • 2.3.1. Criteria for case selection
  • 2.3.2. Modes of comparison
  • 2.3.3. Cross-sectional comparison
  • 2.3.4. Intertemporal comparison
  • 2.3.5. Cross-sectional-intertemporal comparison
  • 2.3.6. Counterfactual comparison
  • 2.3.7. Excursus: The method of agreement and the most different systems design
  • 2.4. The functions of prior knowledge and theory
  • 2.4.1. Specifying the main independent and dependent variable
  • 2.4.2. Substantiating the research hypothesis
  • 2.4.3. Identifying control variables
  • 2.5. Drawing causal inferences for the cases under investigation
  • 2.5.1. Data set results and conclusions
  • 2.5.2. Examples
  • 2.5.3. Concluding remarks
  • 2.6. Measurement and data collection
  • 2.6.1. Conceptualization and measurement in large-N versus small-N research
  • 2.6.2. Determination of classifications and cut-off points
  • 2.6.3. Replicability and measurement error
  • 2.6.4. Data triangulation
  • 2.7. Direction of generalization
  • 2.8. Presenting findings and conclusions
  • 2.9. Example of best practice: Zangl's Judicalization Matters!
  • 2.10. Summary and conclusions
  • 2.11. Appendix: How to make counterfactual analysis more compelling
  • 3. Causal-Process Tracing
  • 3.1. Research goals and research questions
  • 3.1.1. Starting points and research goals
  • 3.1.2. Research goals and functions of causal-process tracing
  • 3.1.3. Research questions
  • 3.2. Ontological and epistemological foundations
  • 3.2.1. Contingency
  • 3.2.2. Causal conditions and configurations
  • 3.2.3. Additive and interactive configurations
  • 3.2.4. Causal conjunctions and causal chains
  • 3.2.5. Social and causal mechanisms
  • 3.2.6. Summary
  • 3.2.7. Appendix: Contexts
  • 3.3. Selecting cases
  • 3.3.1. Misleading advice and trade-offs
  • 3.3.2. General criteria for selecting cases
  • 3.3.3. Specific criteria for selecting cases according to different research goals
  • 3.4. Collecting empirical information
  • 3.5. Drawing causal inferences for the case(s) under investigation
  • 3.5.1. The added value of causal-process observations
  • 3.5.2. Major features of causal-process tracing
  • 3.5.3. Empirical fundaments of CPT: Storylines, smoking guns, and confessions
  • 3.5.4. Logical foundations of CPT I: Causal chains
  • 3.5.5. Logical foundations of CPT II: Process dynamics
  • 3.6. Examples
  • 3.6.1. Brady's Data-Set Observations versus Causal-Process Observations
  • 3.6.2. Skocpol's States and Social Revolutions
  • 3.6.3. Tannenwald's The Nuclear Taboo
  • 3.7. Direction of generalization
  • 3.7.1. Implicit and explicit generalizations
  • 3.7.2. 'Possibilistic' generalization
  • 3.7.3. Drawing conclusions to the sets of causal conditions and configurations
  • 3.7.4. Drawing conclusions to the sets of social and causal mechanisms
  • 3.8. Presenting findings and conclusions
  • 3.9. Summary
  • 4. Congruence Analysis
  • 4.1. Research goals and research questions
  • 4.1.1. Research goals
  • 4.1.2. Research questions
  • 4.2. Ontological and epistemological foundations and affinities
  • 4.2.1. Illustrating the epistemological foundation of the CON approach
  • 4.2.2. Relationships between theories
  • 4.2.3. Implications for the congruence analysis approach
  • 4.3. Selecting theories and cases
  • 4.3.1. Selection and specification of theories
  • 4.3.2. Selection and specification of cases
  • 4.3.3. Crucial cases
  • 4.4. Formulating expectations and collecting data
  • 4.4.1. The specification of propositions
  • 4.4.2. Concrete expectations: Predictions
  • 4.4.3. The collection of information and production of data
  • 4.5. Data analysis - The congruence analysis proper
  • 4.5.1. The steps of the congruence analysis proper
  • 4.5.2. The full set of possible conclusions
  • 4.5.3. Examples: Applications of the congruence analysis proper
  • 4.6. Direction of generalization
  • 4.6.1. Theoretical generalization within a competing theories approach
  • 4.6.2. Theoretical generalization within a complementary theories approach
  • 4.7. Presenting findings and conclusions
  • 4.8. Summary
  • 5. Combining Diverse Research Approaches
  • 5.1. Combining approaches and designs: Purposes and possibilities
  • 5.1.1. Strengthening concept validity of descriptive inference
  • 5.1.2. Strengthening or testing the internal validity of causal inference
  • 5.1.3. Complementing the range of variables, conditions, mechanisms, and theories
  • 5.1.4. Increasing the external validity of causal inferences
  • 5.2. Combining co-variational analysis and causal-process tracing
  • 5.2.1. X-centered combination of COV and CPT
  • 5.2.2. Y-centered combination of cross-case comparisons and CPT
  • 5.3. Combining congruence analysis and causal-process tracing
  • 5.3.1. Causal-process tracing as part of a congruence analysis
  • 5.3.2. Causal-process tracing as an inductive addition to the deductive congruence analysis
  • 5.4. Connecting case studies to large-N studies
  • 5.4.1. Case studies augmenting large-N studies
  • 5.4.2. Case studies preceding large-N studies
  • 5.5. Connecting case studies to medium-N studies
  • 5.5.1. Qualitative Comparative Analysis as a follow-up to case studies
  • 5.5.2. Case studies as a follow-up to a Qualitative Comparative Analysis
  • 5.6. Preconditions for combining different explanatory approaches
  • 5.7. Final remarks
  • Notes
  • Bibliography
  • Index
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