A Generalized Solution for Approximating the Power to Detect Effects of Categorical Moderator Variables Using Multiple Regression

By Herman Aguinis, Robert J. Boik, C. Pierce. 2001

148 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/677c37ed91d005a231b2c60fe61995b9e18c9327

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In Statistics and Probability

Citations in the corpus

Some Common Myths About Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression (2012, background): “Aguinis and Pierce (1998) and Aguinis, Boik, and Pierce (2001) provide a detailed discussion of the determinants of the power of MMRA with categorical moderators; interested readers are directed to their papers.”
Detecting Interaction Effects in Moderated Multiple Regression With Continuous Variables Power and Sample Size Considerations (2009, methodology): “510 empirical examinations that depend exclusively on simulation results, a theory-based approximation to the power of detecting effects of categorical moderator variable was presented by Aguinis, Boik, and Pierce (2001).”
Testing Moderator and Mediator Effects in Counseling Psychology Research. (2004, methodology): “ Aguinis et al. (2001) showed that the power to detect interaction effects in a typical study is .20 to .34, much lower than the recommended level of .80.”
Best-practice recommendations for estimating interaction effects using moderated multiple regression (2010, background): “If there is a sole focus on data-analysis issues, it is likely that the low power problem will not be overcome and researchers are likely to conclude that there is no interaction effect, although the effect may actually be quite strong in the population (Aguinis et al., 2001).”
Improving Our Understanding of Moderation and Mediation in Strategic Management Research (2017, background): “Problem 4: Insufficient Statistical Power Another important issue is statistical power, or the probability of finding moderating effects in a sample when they exist in the population (Aguinis, 2004; Aguinis et al., 2001).”
Sex Differences in Autism Spectrum Disorder: Evidence from a Large Sample of Children and Adolescents (2011, background): “The power of such analyses is affected by several factors including variability of the predictor variables; measurement error; total sample size; and unequal group size (Aguinis et al. 2001).”
"Testing moderator and mediator effects in counseling psychology research": Correction to Frazier et al. (2004). (2004, methodology): “To estimate the sample size needed to have sufficient power (.80) to detect this effect, we entered these parameters into an online power calculator (see Aguinis et al., 2001, and Footnote 5) and determined that we would need a sample of about 160 in each group.”
Understanding and Using Mediators and Moderators (2008, background): “Aguinis et al. (2001) showed that the typical power of ttest for detecting a moderation effect ranges from .20 to .34, which is much lower than the recommended level of .80 (Cohen 1988).”