Common Error 13: Ignoring Nested Model Difference When Interpreting Main Effect Early methodological work addressing multicollinearity concerns in moderation analysis proposed omitting the components (X and M) and testing reduced models containing only the product term (Y = XM; Sharma et al., ...
Interpretation and reporting issues Common Error 12: Lack of Attention of Moderation Effect Size While ΔR² is the appropriate measure of moderation effect size (Chin et al., 2003; Hair et al., 2022), 71.53% of articles in our review either failed to report it or mistakenly relied s ...
Common Error 11: Lack Attention of Lower-Order Interactions in the Three-Way Interaction Model In analyses involving multiple moderators, researchers frequently misinterpret three-way interactions (Becker et al., 2023). Although only 14 of 274 (5.11%) reviewed articles exhibited this specific ...
Common Error 10: Debate on Mean-Center/Standardize X and M Mean-centering in moderation analysis is debated. Its principal value is enhancing coefficient interpretability, not solving multicollinearity (Aguinis et al., 2017; Becker et al., 2023; Dawson, 2014). For continuous variables, center ...
Common Error 9: Opaque or Incorrect Interaction Term Creation with SEM Method The creation of latent variable interaction terms in SEM presents significant challenges in transparency and accuracy (Becker et al., 2023; Rasoolimanesh et al., 2021). Studies show that a majority of CB-SEM (73.47%) ...
Common Error 8: Lack of Attention to Measurement Invariance Across Categories Measurement invariance is essential for valid group comparisons (Leitgöb et al., 2023). Our analysis revealed 13.50% of studies failed to address it. This is critical when using categorical moderators. For exam ...
Common Error 7: Converting a Continuous Variable into Categorical Without Appropriate Justification Researchers sometimes dichotomize continuous moderators to facilitate analysis (Aguinis, 2017; Becker et al., 2023; Rasoolimanesh et al., 2021). Our review found 10.95% of studies used methods ...
Evaluation and analysis issues Common Error 6: Low Statistical Power Moderation tests are known to have lower statistical power than main effect analyses due to various factors (Aguinis et al., 2017; Carte Russell, 2003; Dawson, 2014; Memon et al., 2019). While neglect of this issue ...
Common Error 5: Nonlinear Monotonic Transformations on X, M, and Y Without Rational Justification To meet parametric assumptions (e.g., normality), researchers sometimes apply transformations (e.g., log, square root) to variables X, M, or Y. In our review, 13.50% of studies did so. Arbitrary ...
Measurement, sampling, and scaling issues Common Error 4: Lack of Attention to Measurement Errors of the Interaction Term Measurement error is a primary source of low statistical power in moderation analysis, biasing estimates and attenuating effects (Aguinis, 2017; Carte Russell, 20 ...
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