Putting the pieces together

This book has examined pathway analysis from a number of angles: how to prepare for it by reading the literature in light of its analytic requisites and different ideal types of X1/Y relationships; how to select cases for it based on the expected X1/Y relationships and variation in case characteristics; how to use these tools to gain perspective on cases already selected for process tracing; and how the results from pathway analysis might inform future studies of mechanisms. This chapter seeks to put these pieces together and review the role of pathway analysis in a continuing mixed-method research agenda on mechanisms. The argument is that pathway analysis serves as a critical bridge from what we know about an X1/Y relationship through large-N studies to detailed maps of the mechanisms connecting X1 and Y, which can be used to assess the feasibility of future quantitative studies of mechanisms and the appropriate goals of future qualitative work on mechanisms. From this perspective, good pathway analysis advances the search for mechanisms by systematically building on what we already know about the X1/Y relationship, generating insights into the links between variables, and clarifying avenues of future inquiry.

The role of pathway analysis in the mixed-methods search for mechanisms

A central theme of the book is that pathway analysis constitutes a distinct mode of inquiry. Whereas most research in the social sciences seeks to understand the causes of events or estimate average effects of some variable, X1, on an outcome, Y (Mahoney and Goertz 2006), pathway analysis seeks to (1) understand the mechanisms underlying the X1/Y relationship in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X1/Y relationship. By its very nature, pathway analysis connects the literature on the X1/Y relationship to an ongoing, mixed-method research agenda that first seeks to map mechanisms so that we have a better understanding of the X1/Y relationship and then seeks to inform future, mechanism-centered work.