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Cited by 544

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Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial

Open access
Wang, Y. Andre, Rhemtulla, Mijke·2021·Advances in Methods and Practices in Psychological Science

Abstract

Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample-size rules of thumb that are not backed up by research and power analyses for detecting model misspecification. Missing from most current practices is power analysis for detecting a target effect (e.g., a regression coefficient between latent variables). In this article, we (a) distinguish power to detect model misspecification from power to detect a target effect, (b) report the results of a simulation study on power to detect a target regression coefficient in a three-predictor latent regression model, and (c) introduce a user-friendly Shiny app, pwrSEM, for conducting power analysis for detecting target effects in structural equation models.

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Metadata

Authors
Wang, Y. A., & Rhemtulla, M.
Venue
Advances in Methods and Practices in Psychological Science
Published
2021-01
Volume
4
Issue
1
ISSN
  • 2515-2459 (ISSN-L)
  • 2515-2467
OpenAlex
W3027373163
Cited by
544
References
11
FWCI
54.52
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial. https://app.cochat.ai/papers/10.1177/2515245920918253
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Wang, Y. A., & Rhemtulla, M. (2021). Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920918253

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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.