Beyond addressing multicollinearity: Robust quantitative analysis and machine learning in international business research

We reconcile the recommendations made by Kalnins (J Int Bus Stud, 2022) on the one hand and by Lindner, Puck and Verbeke (J Int Bus Stud 51(3):283-298, 2020) on the other, on how international business (IB) quantitative researchers should treat multicollinearity. We explain that, in principle, treatment depends on the underlying data generation process, but note that datasets based on any single generation process are rare. In doing so, we broaden the discussion to include how research methods should be selected and robust statistical models built. In addition, we highlight the importance of a comprehensive literature review in selecting appropriate control variables. We also make suggestions on addressing cross-level dependencies and selecting robustness checks to avoid bias in statistical results. Finally, we go beyond regression and include a broader palette of research methodologies building on machine-learning approaches.

Automated summary

This article compares two studies on the treatment of multicollinearity in quantitative research in international business and provides suggestions from aspects such as data generation process, research method selection, and robustness checks.