Pathway Analysis and the Search for Causal Mechanisms. (August 2016)

• Record Type: Journal Article
• Authors: Weller, Nicholas; Barnes, Jeb
• Other Names: Elman Colin guest-editor.; Gerring John guest-editor.; Mahoney James guest-editor.
• Is Part Of: Sociological methods & research. Volume 45:Number 3(2016:Aug.)
• Page Start: 424
• Page End: 457
• Publication Date: 2016-08
• Subjects: causal mechanisms -- mixed-method research -- case selection -- hypotheses generation -- qualitative methods; Sociology -- Research -- Periodicals; Sociology -- Methodology -- Periodicals; 301.072
• DOI: 10.1177/0049124114544420
• Languages: English
• ISSNs: 0049-1241

Abstract

The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting cases to study causal mechanisms. We outline a general approach for selecting cases for pathway analysis: a mode of qualitative research that is part of a mixed-method research agenda, which seeks to (1) understand the mechanisms or links underlying an association between some explanatory variable, X 1, and an outcome, Y, in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X 1/ Y relationship. The gist of our approach is that researchers should choose cases for comparison in light of two criteria. The first criterion is the expected relationship between X 1/ Y, which is the degree to which cases are expected to feature the relationship of interest between X 1 and Y . The second criterion is variation in case characteristics or the extent to which the cases are likely to feature differences in characteristics that can facilitate hypothesis generation. We demonstrate how to apply our approach and compare it to a leading example of pathway analysis in the so-called resource curse literature, a prominent example of a correlation featuring a nonlinear relationship and multiple causal mechanisms.