Identifying and Using Nonlinear and Interactive Control Variables
研究发现管理学期刊中仅3%的研究包含非线性和交互控制变量，而遗漏它们可能扭曲统计检验和效应估计，甚至逆转结论。文章提出五步指南帮助研究者系统识别和纳入这些控制变量，以提升因果推断的有效性和实证结果的稳健性。
Nonlinear and interactive effects (NIEs) are central to management theory. Consequently, although researchers commonly include linear control variables, the omission of nonlinear and interactive control variables (NICs) can lead to incorrect conclusions because the omission can distort statistical tests and effect-size estimates. We reviewed 548 quantitative articles published between 2021 and 2023 in Academy of Management Journal, Journal of Management, and Strategic Management Journal. We discovered that about 73% tested for NIEs, but only 3% included NICs. Also, by reanalyzing a published study, we demonstrate that the exclusion of theoretically relevant NICs can reverse substantive conclusions, highlighting the threat such omissions pose to theory advancement. To address this methodological challenge, we introduce a five-step guide for systematically identifying, evaluating, and integrating NICs into research involving NIEs. The guide offers a structured, theory-driven approach that uses associations among model variables and their linear controls to determine which NICs are critical for unbiased estimation of NIEs. We also explain how to avoid over-control, maintain statistical efficiency, and transparently manage omitted NICs. Applying the five-step approach strengthens the validity of causal inference in studies of nonlinear and interactive effects and enhances the robustness of empirical results. In addition to improving estimation accuracy, the systematic and theory-based inclusion of NICs advances theory development by clarifying boundary conditions, distinguishing competing explanations, and enabling the cumulative integration of empirical results.