No abstract

How can scholars select cases from a large universe for in-depth case study analysis? Random sampling is not typically a viable approach when the total number of cases to be selected is small. Hence attention to purposive modes of sampling is needed. Yet, while the existing qualitative literature on case selection offers a wide range of suggestions for case selection, most techniques discussed require in-depth familiarity of each case. Seven case selection procedures are considered, each of which facilitates a different strategy for within-case analysis. The case selection procedures considered focus on typical, diverse, extreme, deviant, influential, most similar, and most different cases. For each case selection procedure, quantitative approaches are discussed that meet the goals of the approach, while still requiring information that can reasonably be gathered for a large number of cases.

It is important to know what wealthy Americans seek from politics and how (if at all) their policy preferences differ from those of other citizens. There can be little doubt that the wealthy exert more political influence than the less affluent do. If they tend to get their way in some areas of public policy, and if they have policy preferences that differ significantly from those of most Americans, the results could be troubling for democratic policy making. Recent evidence indicates that “affluent” Americans in the top fifth of the income distribution are socially more liberal but economically more conservative than others. But until now there has been little systematic evidence about the truly wealthy, such as the top 1 percent. We report the results of a pilot study of the political views and activities of the top 1 percent or so of US wealth-holders. We find that they are extremely active politically and that they are much more conservative than the American public as a whole with respect to important policies concerning taxation, economic regulation, and especially social welfare programs. Variation within this wealthy group suggests that the top one-tenth of 1 percent of wealth-holders (people with $40 million or more in net worth) may tend to hold still more conservative views that are even more distinct from those of the general public. We suggest that these distinctive policy preferences may help account for why certain public policies in the United States appear to deviate from what the majority of US citizens wants the government to do. If this is so, it raises serious issues for democratic theory.

Typologies are well-established analytic tools in the social sciences. They can be “put to work” in forming concepts, refining measurement, exploring dimensionality, and organizing explanatory claims. Yet some critics, basing their arguments on what they believe are relevant norms of quantitative measurement, consider typologies old-fashioned and unsophisticated. This critique is methodologically unsound, and research based on typologies can and should proceed according to high standards of rigor and careful measurement. These standards are summarized in guidelines for careful work with typologies, and an illustrative inventory of typologies, as well as a brief glossary, are included online.

Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation.