Designing Social Inquiry: Scientific Inference in Qualitative Research (or KKV, as it is known) is a famous 1994 book written by Gary King, Robert Keohane, and Sidney Verba, all of Harvard University. Designing Social Inquiry: Scientific Inference in Qualitative Research lays out guidelines for conducting qualitative research. The central thesis of the book is that qualitative and quantitative research share the same “logic of inference.” Designing Social Inquiry: Scientific Inference in Qualitative Research applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of research. If you were doing some sort of Qualitative research, would you use this method?
Designing Social Inquiry: Scientific Inference in Qualitative Research Summary
The goal of Designing Social Inquiry: Scientific Inference in Qualitative Research is to guide researchers in producing valid causal inferences in social science research. Designing Social Inquiry: Scientific Inference in Qualitative Research primarily applies lessons from regression-oriented quantitative analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of research. The authors argue, “whether we study many phenomena or few… the study will be improved if we collect data on as many observable implications of our theory as possible.” (Page 8)
The authors note that case studies do not necessarily have to be number of one, or a few cases. A case study can include many observations within a case (many individuals and entities across many periods). (Page 9) In Designing Social Inquiry: Scientific Inference in Qualitative Research King, Keohane, and Verba, criticize Harry H. Eckstein’s notion of “crucial case studies”, warning that a single observation makes it harder to estimate multiple causal effects, more likely that there is measurement error, and risks that an event in a single case was caused by random error. (Page 10)
The argument goes that a valid research design requires both qualitative and quantitative research, a research question that poses an important question that will contribute to the knowledge about this subject, and a comprehensive literature review from which theory-driven hypotheses are drawn. While qualitative methods, unlike quantitative methods, cannot produce precise measurements of uncertainty about the conclusions, qualitative scholars should give indications about the uncertainty of their inferences. King, Keohane, and Verba argue that “the single most serious problem with qualitative research in political science is the pervasive failure to provide reasonable estimates of the uncertainty of the investigator’s inferences.” (Page 11)
According to Designing Social Inquiry: Scientific Inference in Qualitative Research, the rules for good causal theories are that they need to:
- be falsifiable
- have internal consistency (generate hypotheses that do not contradict each other)
- have variation (explanatory variables should be exogenous and dependent variables should be endogenous)
- have “concrete” and observable concepts
- have “leverage,” and explain much with little
- In terms of case selection, King, Keohane, and Verba warn against “selecting on the dependent variable.”
Conclusion
Designing Social Inquiry: Scientific Inference in Qualitative Research was not universally accepted, though it is well-used. Designing Social Inquiry: Scientific Inference in Qualitative Research gives an approach to qualitative and quantitative research in political science, showing how the same logic of inference underlies both. This book discusses issues related to framing research questions, measuring the accuracy of data and the uncertainty of empirical inferences, discovering causal effects, getting the most out of qualitative research, interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and measurement errors. There is a minimum of mathematical notation in the book.
