9781137271327

1137271329

9781349316571

1349316571

9780230240704

INGO ROHLFING is Assistant Professor for Comparative Social Research at the Cologne Graduate School in Management, Economics and Social Sciences, University of Cologne, Germany. His research is centred on party politics and social science methodology and he has published in journals such as Comparative Political Studies and West European Politics.

Electronic book text.

Introduction Case, Case Study, and Causality: Core Concepts and Fundamentals Types of Case Study and Case Selection Forms and Problems of Comparisons Enhancing Causal Inference in Comparisons Process Tracing: Theory, Temporality, and Method From Evidence to Inference: Uses of Sources and Counterfactuals Frequentist and Bayesian Causal Inference in Tests of Hypotheses External Validity: Challenges and Strategies Conclusion: Guided by Theory, Moving Forward Step by Step.

A discussion of the case study method which develops an integrative framework for causal inference in small-n research. This framework is applied to research design tasks such as case selection and process tracing. The book presents the basics, state-of-the-art and arguments for improving the case study method and empirical small-n research. Case Studies and Causal Inference delivers a self-contained and balanced discussion of the case study method on the basis of an integrative, but clear-cut framework for small-n research. It deals with case studies that develop new hypotheses for hitherto unexplained phenomena, perform classic hypothesis testing, and address puzzles in order to refine existing hypotheses. The framework further covers small-n research concerned with a range of causal effects (correlations and set relations), causal mechanisms, and the integration of both in causal explanations. By applying the framework to all steps in the research process, Rohlfing demonstrates how it can contribute to the rigor of empirical case studies and the advancement of social science theory. On the methodological side, the integrative perspective forms the basis for improving the case study method in multiple directions. Throughout the book, arguments are illustrated with numerous empirical examples from political science, public administration, and sociology.

Review: 'Ingo Rohlfing's book marks an important contribution to the case-methods literature. Building upon but going beyond earlier work, he advances an integrative perspective that offers a comprehensive discussion of case studies and their potential for causal inference. Foundational questions on the case method - the nature of causation (effects or mechanisms), scope conditions, the role(s) of theory, and process tracing - are addressed, and done so in a user-friendly manner where conceptual discussions are illuminated with a rich set of empirical illustrations. Case Studies and Causal Inference is a must read - not just for methodologists, but for all qualitatively oriented social scientists.' -Jeffrey T. Checkel, Simons Chair in International Law and Human Security, Simon Fraser University, Research Professor, Peace Research Institute, Oslo An important and ambitious effort to synthesize different approaches to causal inference in case study research. The book offers a balanced discussion of different methodologies, and it reaches sophisticated conclusions that move the discussion forward. Rohlfing's work is useful for both teaching methods and designing substantive case study research James Mahoney, Fitzgerald Professor of Economic History, Northwestern University, USA.

Research methods series

Cover; Case Studies and Causal Inference; Contents; List of Figures ; List of Tables ; Preface ; 1 Introduction ; 2 Case, Case Study, and Causation: Core Concepts and Fundamentals ; 3 Types of Case Studies and Case Selection ; 4 Forms and Problems of Comparisons ; 5 Enhancing Causal Inference in Comparisons.

6 Process Tracing: Theory, Temporality, and Method 7 From Evidence to Inference: Use of Sources and Counterfactuals ; 8 Frequentist and Bayesian Causal Inference in Tests of Hypotheses ; 9 External Validity and Generalization: Challenges and Strategies.

10 Conclusion: Guided by Theory, Moving Forward Step by Step Notes ; References ; Index.