Misconceptions about multicollinearity in international business research: Identification, consequences, and remedies

• 成果类型: Editorial Material
• 署名作者: Lindner, Thomas; Puck, Jonas; Verbeke, Alain
• 署名单位: Vienna University of Economics & Business; University of Calgary; University of Reading; Vrije Universiteit Brussel
• 刊物名称: JOURNAL OF INTERNATIONAL BUSINESS STUDIES
• ISSN/ISSBN: 0047-2506
• DOI: 10.1057/s41267-019-00257-1
• 发表日期: 2020
• 页码: 283-298
• 关键词: multicollinearity; collinearity; regression analysis; hierarchical modeling; quantitative research methods

摘要

Collinearity between independent variables is a recurrent problem in quantitative empirical research in International Business (IB). We explore insufficient and inappropriate treatment of collinearity and use simulations to illustrate the potential impact on results. We also show how IB researchers doing quantitative work can avoid collinearity issues that lead to spurious and unstable results. Our six principal insights are the following: first, multicollinearity does not introduce bias. It is not an econometric problem in the sense that it would violate assumptions necessary for regression models to work. Second, variance inflation factors are indicators of standard errors that are too large, not too small. Third, coefficient instability is not a consequence of multicollinearity. Fourth, in the presence of a higher partial correlation between the variables, it can paradoxically become more problematic to omit one of these variables. Fifth, ignoring clusters in data can lead to spurious results. Sixth, accounting for country clusters does not pick up all country-level variation.