How to get away with multicollinearity: A users’ guide

Résumé

This paper explains how to detect and overcome multicollinearity problems. In particular, we describe four procedures to handle high levels of correlation among explanatory variables: (1) to check variables coding and transformations; (2) to increase sample size; (3) to employ some data reduction technique and (4) to check specific literature on the subject. Methodologically, the research design uses basic simulation to show how multicollinearity affects coefficients efficiency. In addition, we adopted TIER 2.0 documentation protocol in order to increase transparency and to ensure results replicability. We argue that significant progress can occur in our discipline if scholars check their data for multicollinearity using the checklist presented in this article.