Designing Social Inquiry: Scientific Inference in Qualitative Research
by Gary King, Robert O. Keohane, Sidney Verba
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Book Summary Information
Author: Gary King, Robert O. Keohane, Sidney VerbaEdition: Paperback
Audio: English (Unknown); English (Original Language); English (Published)
Published: 1994-05-02
ISBN: 0691034710
Number of pages: 300
Publisher: Princeton University Press
Book Reviews of Designing Social Inquiry: Scientific Inference in Qualitative Research
Book Review: Misunderstanding qualitative inquiry
Summary: 1 Stars
Several reviewers have commented that this book basically applies quantitative and statistical reasoning to qualitative research, and that the authors make some major errors in doing so. I don't want to address their misunderstandings of statistics, which other reviewers have identified, but to point out that, contrary to those reviewers who think this book "is excellent as a guide to what an optimal qualitative research design should be," KK&V fundamentally misunderstand qualitative research, and attempt to force this into a quantitative framework that completely misses the actual logic and rigor of qualitative inquiry. Their book has, however, been quite influential and controversial, and has prompted several major rejoinders within political science that attempt to correct KK&V's misunderstandings (Henry Brady & David Collier, Rethinking Social Inquiry; Alexander George and Andrew Bennett, Case Studies and Theory Development in the Social Sciences; Gary Goertz & Jack Levy (Eds.), Explaining War and Peace: Case Studies and Necessary Condition Counterfactuals).
Understanding this debate requires noting a peculiarity of qualitative research in political science (often called "case study research" in this field): that qualitative researchers in political science often describe what they're doing in terms of "variables." In almost all other fields, qualitative researchers don't think of what they're doing in terms of variables, but in terms of events and processes. This difference is connected to two quite different ways of understanding causality: the "regularity" view (derived from David Hume's analysis of causality) that defines causality as simply the regular association of variables, and denies that there is anything beyond this (the standard view in philosophy for much of the 20th century), and a "realist," "process," or "causal/mechanical" approach, which has become prominent in philosophy more recently, that sees causality as the actual mechanisms and processes by which one event or phenomenon influences another. I don't have the space to go into this issue in the detail it requires (in philosophy, see Wesley Salmon, Causality and Explanation; Peter Manicas, A Realist Philosophy of Social Science: Explanation and Understanding; for the implications for qualitative research, see my paper "Causal Explanation, Qualitative Research, and Scientific Inquiry in Education," published in Educational Researcher 33(2), pp. 3-11, March 2004). The point is that imposing a "variable" or "regularity" understanding of causality on qualitative research completely misconceives the way in which most qualitative researchers think about what they're doing. For a more accurate understanding of qualitative research design, see my book Qualitative Research Design: An Interactive Approach, or Catherine Marshall & Gretchen Rossman, Designing Qualitative Research.
Joe Maxwell
Summary of Designing Social Inquiry: Scientific Inference in Qualitative Research
While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have produced a farsighted and timely book that promises to sharpen and strengthen a wide range of research performed in this field. These leading scholars, each representing diverse academic traditions, have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. Their book demonstrates that the same logic of inference underlies both good quantitative and good qualitative research designs, and their approach applies equally to each.
Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and uncertainty of empirical inferences, discovering causal effects, and generally improving qualitative research. Among the specific topics they address are interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed. The unified logic of inference that this book explicates will be enormously useful to qualitative researchers of all traditions and substantive fields.
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