Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?

The classic work on qualitative methods in political science Designing Social Inquiry presents a unified approach to qualitative and quantitative research in political science, showing how the same logic of inference underlies both. This stimulating book discusses issues related to framing research questions, measuring the accuracy of data and the uncertainty of empirical inferences, discovering causal effects, and getting the most out of qualitative research. It addresses topics such as interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. The book only uses mathematical notation to clarify concepts, and assumes no prior knowledge of mathematics or statistics. Featuring a new preface by Robert O. Keohane and Gary King, this edition makes an influential work available to new generations of qualitative researchers in the social sciences.

With innovative new chapters on process tracing, regression analysis, and natural experiments, the second edition of Rethinking Social Inquiry further extends the reach of this path-breaking book. The original debate with King, Keohane, and Verba_now updated_remains central to the volume, and the new material illuminates evolving discussions of essential methodological tools. Thus, process tracing is often invoked as fundamental to qualitative analysis, but is rarely applied with precision. Pitfalls of regression analysis are sometimes noted, but often are inadequately examined. And the complex assumptions and trade-offs of natural experiments are poorly understood. The second edition extends the methodological horizon through exploring these critical tools. A distinctive feature of this edition is the online placement of four chapters from the prior edition, all focused on the dialogue with King, Keohane, and Verba. Also posted online are exercises for teaching process tracing and understanding process tracing.

“This is an excellent addition to the literature of integrated methodology. The author has skillfully integrated diverse ways of thinking about mixed methods into a comprehensive and meaningful framework. By providing detailed examples, she makes it easy for both the students and the practitioners to understand the intricate details and complexities of doing mixed methods research. On the other hand, by comparing, contrasting, and bridging multiple perspectives about mixed methods, she has made this book very relevant and useful to seasoned scholars of mixed methodology.”--Abbas Tashakkori, Frost Professor and coordinator, educational research and evaluation methodology, Department of Educational and Psychological Studies, Florida International University, founding coeditor, Journal of Mixed Methods Research

The bestseller that pioneered the comparison of qualitative, quantitative, and mixed methods research design continues in its Fourth Edition to help students and researchers prepare their plan or proposal for a scholarly journal article, dissertation or thesis.

The first comprehensive guide to natural experiments, providing an ideal introduction for scholars and students.