How to get away with multicollinearity: A users’ guide

Dalson Figueiredo Filho, Lucas Silva, Enivaldo Rocha, Amanda Domingos


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.

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Direitos autorais 2016 Revista Política Hoje - ISSN: 0104-7094

Licença Creative Commons
Esta obra está licenciada sob uma licença Creative Commons Atribuição 4.0 Internacional.

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