Essay on Designing Social Inquiry : Scientific Inference

Essay on Designing Social Inquiry : Scientific Inference

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King, Keohane, and Verba (1994) in “Designing Social Inquiry: Scientific Inference in Qualitative Research” attempt to unify political science under a single logic of inquiry based on quantitative regression analysis. While initially divisive even somewhat offensive to qualitative scholars, this debate culminated in greater scrutiny of qualitative methods and the delineation of the key advantages and limitations of both qualitative and quantitative methods. In the end, KKV’s attempt to unite the field provided an opportunity for advances and expansions in both quantitative and qualitative methodologies, greatly expanding and enhancing the comparativist’s methodological toolbox. Nevertheless, in trying to unite the discipline under a single logic of inference, KKV overlooked the unique and complementary contributions as well as the relative strengths and limitations of quantitative and qualitative analysis. More recent scholarship delineates exactly what each methodological tool kit brings to the comparativist’s table and how a mixed-method approach can yield higher quality research within comparative politics.

This response proceeds as follows: In the first section, I define what it means to “infer causal effects” and what the available methodological tools are in comparative politics. In the second section, I compare the relative strengths and weaknesses of statistical analysis with a case study approach. In the third section, I provide some examples from the field that utilize these approaches. In the final section, I explore some of the more recent advances in comparative politics methodology and argue unequivocally for the benefits of a mixed-methods or nested approach in comparative politics as a way to produce the hi...

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... population to be used for theory building and testing (Gerring and Seawright 2007). For example, when developing theory, Lieberman recommends selecting at least one case that is not well predicted by the preliminary regression model (i.e., an “offline” case). Outlier cases are recommended because of their utility for exposing missing variables that can then be brought into a subsequent round of regression analysis. Likewise, for model testing, process tracing and CPOs can retest regression findings. For this cases that are well predicted by the statistical model (i.e., “online” cases) should be used to test if they follow the causal pattern in the regression analysis, indicating that the model is correctly specified (Lieberman 2005). By using these tools in tandem, many of the limitations of the methods when used separately can be overcome.

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