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.

我们一方面协调 Kalnins (J Int Bus Stud, 2022) 和 Lindner、Puck 和 Verbeke (J Int Bus Stud 51(3):283–298, 2020) 提出的关于国际业务如何开展的建议。 (IB) 定量研究人员应该处理多重共线性。我们解释说，原则上，处理取决于基础数据生成过程，但请注意，基于任何单一生成过程的数据集很少见。在此过程中，我们扩大了讨论范围，包括应如何选择研究方法和建立稳健的统计模型。此外，我们强调了全面的文献回顾在选择适当的控制变量方面的重要性。我们还提出了解决跨级别依赖关系和选择稳健性检查以避免统计结果偏差的建议。最后，