Nested Analysis as a Mixed-Method Strategy for Comparative Research

TLDR

Despite repeated calls for the use of “mixed methods” in comparative analysis, political scientists have few systematic guides for carrying out such work. The paper proposes a unified approach that combines intensive case‑study analysis with statistical analysis. The nested design uses statistical analysis to guide case selection, test hypotheses from small‑N studies, and refine measurement, while small‑N case studies assess the plausibility of statistical relationships, generate theory from outliers, and improve measurement strategies. The integrated strategy enhances the ability to make valid causal inferences in cross‑national comparative research by leveraging the strengths of both case‑study and statistical approaches.

Abstract

Despite repeated calls for the use of “mixed methods” in comparative analysis, political scientists have few systematic guides for carrying out such work. This paper details a unified approach which joins intensive case-study analysis with statistical analysis. Not only are the advantages of each approach combined, but also there is a synergistic value to the nested research design: for example, statistical analyses can guide case selection for in-depth research, provide direction for more focused case studies and comparisons, and be used to provide additional tests of hypotheses generated from small-N research. Small-N analyses can be used to assess the plausibility of observed statistical relationships between variables, to generate theoretical insights from outlier and other cases, and to develop better measurement strategies. This integrated strategy improves the prospects of making valid causal inferences in cross-national and other forms of comparative research by drawing on the distinct strengths of two important approaches.