.png){kind=icon}
.png){kind=icon}
Return
Identifying and Using Nonlinear and Interactive Control Variables
.png){kind=icon}
PRE
AI
DOI：10.1177/01492063261431571.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
<jats:p>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.</jats:p>
Journal
J
IF:
9
Papers:
3.8K
Citations:
3.7W