Situational strength interactions: Are variance patterns consistent with the theory?

Mischel's (1973, 1977) theory of situational strength has been used widely within the organizational sciences to help explain why contextual factors moderate predictor-criterion relationships. Situational strength interactions represent a particular type known as a restricted variance (RV) interaction (Cortina, Köhler, & Nielsen, 2015). The theory proposes that the strength of a given situation constrains or compresses the variance of the dependent variable, weakening its prediction from other variables. Other theories and models, such as self-determination theory and the job characteristics model, also make implicit references to variance compression based on the level of autonomy in a given situation and to the interactions that this compression creates. It is unclear, however, whether differences in strength (or degree of constraint) actually yield variance differences that are consistent with the theoretical framework. In this meta-analysis, we reviewed 132 articles that imply RV effects, 100 of which allowed for variance comparisons. We found that only a handful of authors explicitly connect their theoretical arguments and interaction hypotheses to changes in variance in the Introduction section of their articles. Moreover, our findings also reveal that, for the most part, variance differences between weak and strong situations are minimal. Where differences exist, they are often in the opposite of the expected direction (i.e., larger variance in "strong" situations). We discuss our findings and their theoretical and practical implications. We also provide recommendations for designing studies and testing for such interactions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Analyzing matrices of meta-analytic correlations: current practices and recommendations.

Researchers have become increasingly interested in conducting analyses on meta-analytic correlation matrices. Methodologists have provided guidance and recommended practices for the application of this technique. The purpose of this article is to review current practices regarding analyzing meta-analytic correlation matrices, to identify the gaps between current and best practices, and to offer a comprehensive set of recommendations regarding the planning, collection, analysis, and interpretation of studies that utilize meta-analytic correlation matrices. Copyright © 2016 John Wiley & Sons, Ltd.