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Cited by 458

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

Statistical power problems with moderated multiple regression in management research

Aguinis, H·1995·Journal of Management

Abstract

Due to the increasing importance of moderating (i.e., interaction) effects, the use of moderated multiple regression (MMR) has become pervasive in numerous management specialties such as organizational behavior, human resources management, and strategy, to name a few. Despite its popularity, recent research on the MMR approach to moderator variable detection has identified several factors that reduce statistical power below acceptable levels and, consequently, lead researchers to erroneously dismiss theoretical models that include moderated relationships. The present article (1) briefly describes MMR, (2) reviews factors that affect the statistical power of hypothesis tests conducted using this technique, (3) proposes solutions to low power situations, and (4) discusses areas and problems related to MMR that are in need of further investigation. If we want to know how well we are doing in the biological, psychological, and social sciences, an index that will serve us well is how far we have advanced in our understanding of the moderator variables of our field -Hall & Rosenthal, 1991, p. 447.

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Metadata

Authors
Aguinis, H.
Venue
Journal of Management
Published
1995
Volume
21
Issue
6
Pages
1141-1158
ISSN
0149-2063 (ISSN-L)
OpenAlex
W2014770296
Cited by
458
References
67
FWCI
10.17
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Statistical power problems with moderated multiple regression in management research. https://app.cochat.ai/papers/10.1016/0149-2063(95)90026-8
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Aguinis, H. (1995). Statistical power problems with moderated multiple regression in management research. Journal of Management. https://doi.org/10.1016/0149-2063(95)90026-8

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.