semPower: General power analysis for structural equation models

Structural equation modeling (SEM) is a widespread and commonly used approach to test substantive hypotheses in the social and behavioral sciences. When performing hypothesis tests, it is vital to rely on a sufficiently large sample size to achieve an adequate degree of statistical power to detect t...

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Bibliographic Details
Published in:Behavior research methods Vol. 56; no. 4; pp. 2901 - 2922
Main Authors: Moshagen, Morten, Bader, Martina
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2024
Subjects:
Behavioral Science and Psychology
Cognitive Psychology
Data Interpretation, Statistical
Factor Analysis, Statistical
Humans
Latent Class Analysis
Models, Statistical
Original Manuscript
Psychology
Sample Size
Software
Confirmatory factor analysis
Model evaluation
Structural equation modeling
Sample size planning
Statistical power
ISSN:1554-3528, 1554-351X, 1554-3528
Online Access:Get full text
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Summary:Structural equation modeling (SEM) is a widespread and commonly used approach to test substantive hypotheses in the social and behavioral sciences. When performing hypothesis tests, it is vital to rely on a sufficiently large sample size to achieve an adequate degree of statistical power to detect the hypothesized effect. However, applications of SEM rarely consider statistical power in informing sample size considerations or determine the statistical power for the focal hypothesis tests performed. One reason is the difficulty in translating substantive hypotheses into specific effect size values required to perform power analyses, as well as the lack of user-friendly software to automate this process. The present paper presents the second version of the R package semPower which includes comprehensive functionality for various types of power analyses in SEM. Specifically, semPower 2 allows one to perform both analytical and simulated a priori, post hoc, and compromise power analysis for structural equation models with or without latent variables, and also supports multigroup settings and provides user-friendly convenience functions for many common model types (e.g., standard confirmatory factor analysis [CFA] models, regression models, autoregressive moving average [ARMA] models, cross-lagged panel models) to simplify power analyses when a model-based definition of the effect in terms of model parameters is desired.
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ISSN:1554-3528
1554-351X
1554-3528
DOI:10.3758/s13428-023-02254-7