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Designing Social Inquiry: Scientific Inference in Qualitative Research, by Gary King, Robert O. Keohane, Sidney Verba
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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.
- Sales Rank: #451073 in eBooks
- Published on: 1994-05-02
- Released on: 1994-05-02
- Format: Kindle eBook
Review
"The book is marked by a very careful building up of all concepts; by clear, vivid writing; and by an excellent use of extended examples from the work of such scholars as Nina Halpern, Atul Kohli, and David Laiting."--Journal of Politics
From the Back Cover
"This book has a lot to offer any and all researchers-from senior professional veterans to thesis newcomers at the undergraduate and graduate levels. . . . The authors provide so many examples from current research that the reader can devise strategies for getting the most leverage out of her own research. A must read."--Peter Gourevitch, University of California at San Diego
Most helpful customer reviews
6 of 13 people found the following review helpful.
KKV
By Kevin MacG Adams
KKV is the book behind the excellent paper by Gerardo L. Munck entitled Canons of Research Design in Qualitative Analysis [Studies in Comparative Literature, Vol. 33, No. 3 (Fall 1998), p. 18-45].
The book has the details although it is harder to read than Munck's excellent paper. This is a must read for all doing Qualitative Research.
37 of 40 people found the following review helpful.
This not a stats book
By Amazon Customer
This is a response to reviewers who think this is a stat book. This book is not meant to serve as a stats textbook (if you want one there are plenty of good ones written by statisticians and econometricians). This book is designed to serve as a guide to research design in social science in terms of developing a question, following systematic research procedures and measurement while using qualitative research methods. In that regard it does not do a great job as they are stuck up with applying simplistic statistical techniques (predominantly regression analysis)to qualitative methodology. As a result the work ends up appearing weak to both the statistically inclined (including myself) and those who use predominantly qualitative methods. Arguably the biggest problems with this work is in their treatment of constant dependent variable designs. This arises from their notion of a "causal effect" that is quite different from what qualitative researchers might see as causality. In statistical terms their notion is correct but when we move towards a qualitative interpretation of the same the concept becomes problematic primarily because it is difficult to discern the appropriate differentation between values of the dependent variable in qualitative work.
Nonetheless, this book should be treated on its own terms for attempting to synthesize quantiative and qualitative research methods. This book started a controversy that continues till this day and did a great job in forcing people to actually think more deeply about their research design and methods.
If you want to study statistics or econometrics forget this book (choose what you want to know about....regression analysis, time-series models, bayesian models....your choice). If you want to study qualitative research well read this book but then read Brady and Collier 2002 and George and Bennett 2004. George and Bennett's work is arguably the best book on research design I have ever read.
22 of 25 people found the following review helpful.
All the advantages and disadvantages of statistical reasoning applied to qualitative political science
By Arthur Digbee
This book takes the basic logic of statistical inference and applies it to qualitative research design in political science. As several reviewers note, it is not a book on statistics, nor indeed does it pretend to be. However, it extends the logic of statistical research design into nonquantitative research.
That much it does very well. By thinking about how to test hypotheses and how to increase variation in a qualitative research design, it has been very influential. Most important, it has sparked extensive criticism, modifying and delineating its claims.
The book has some amusing flaws. Most of the examples come from the authors' colleagues and graduate students at Harvard, which suggests either that good research is not done by people without that connection or that the authors don't read anything written by people who don't have an office down the hall. The two non-quantitative coauthors have both done extensive qualitative research that demonstrably violates the advice given here--both before and after this book. This is evidence that the advice is hard to follow, that they have not read the book, or that good scholars take other factors into consideration when designing research.
The last hypothesis is in fact the right one. There are many factors that go into good research design, and positivistic hypothesis testing provides only a few. Even many of the examples they give are less appropriate than appears at first glance, addressing evidence that goes well beyond what this book's advice would be.
In short, don't rely on this as a bible. Don't believe its claims that all good research must meet these standards. Still, it's a good handbook for what it seeks to accomplish.
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