The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial
Open accessAbstract
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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Citations
Cites
Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615
lavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955
Practical issues in structural modeling
BENTLER, P. M., & CHOU, C.-P. (1987) · Sociological Methods & Research · doi:10.1177/0049124187016001004 · Cited by 5,804
Sample size in factor analysis.
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999) · Psychological Methods · doi:10.1037/1082-989x.4.1.84 · Cited by 5,497 · cited 1×
“However, these rules of thumb do not always agree with each other, have little empirical support ( Jackson, 2003; MacCallum & Austin, 2000; MacCallum et al., 1999), and generalize to only a small range of model types (Marsh et al., 1998).”
BackgroundRaise standards for preclinical cancer research
Begley, C. G., & Ellis, L. M. (2012) · Nature · doi:10.1038/483531a · Cited by 2,979
How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power
Muthén, L. K., & Muthén, B. O. (2002) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0904_8 · Cited by 2,362
Reporting practices in confirmatory factor analysis: an overview and some recommendations
Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009) · Psychological Methods · doi:10.1037/a0014694 · Cited by 2,242
Determining power and sample size for simple and complex mediation models
Schoemann, A. M., Boulton, A. J., & Short, S. D. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550617715068 · Cited by 1,837
Nosek (2012) · doi:10.1177/1745691612459058 · Cited by 1,301
Statistically controlling for confounding constructs is harder than you think
Westfall, J., & Yarkoni, T. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152719 · Cited by 547
Cited by
semPower: General power analysis for structural equation models
Moshagen, M., & Bader, M. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02254-7 · Cited by 308
“…of options providing either analytical (Jak et al., 2021; Miles, 2003; Z. Zhang & Yuan, 2018) or simulated power analysis (Muthén & Muthén, 2002; Wang & Rhemtulla, 2021) allow for a generic model-based definition of the effect, but these require the manual definition of the values of all…”
BackgroundInfluentialSommet, N., Weissman, D. L., Cheutin, N., & Elliot, A. (2022) · doi:10.31219/osf.io/xhe3u · Cited by 36
Purchase Intentions in a Chatbot Environment: An Examination of the Effects of Customer Experience
Martínez Puertas, S., Illescas Manzano, M. D., Segovia López, C., & Ribeiro-Cardoso, P. (2024) · Oeconomia Copernicana · doi:10.24136/oc.2914 · Cited by 34
Barlow, I. U., Lee, E., & Saling, L. (2024) · European Eating Disorders Review · doi:10.1002/erv.3032 · Cited by 31
Scafuto, F., Quinto, R. M., Ghiroldi, S., Montecucco, N. F., Presaghi, F., Iani, L., & De Vincenzo, F. (2024) · Current Psychology · doi:10.1007/s12144-024-06081-7 · Cited by 16
Pirbaglou, M., El Morr, C., Ahmad, F., & Ritvo, P. (2025) · JMIR Mental Health · doi:10.2196/65853 · Cited by 6
“Considering that structural equation modeling is a large-sample method [74,75], we consider higher P values in separate analyses of the 2017 and 2018 cohorts to be a reflection of smaller sample sizes.”
Stone, B. M. (2025) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-023-01151-6 · Cited by 3
Department of Psychology, F. O. A., Ptáček, M., & Jelínek, M. (2024) · Ceskoslovenska Psychologie · doi:10.51561/cspsych.68.1.30 · Cited by 3
“…on the data underlying the present study, researchers should consider utilising Monte Carlo simulations before the study to obtain better estimations of the appropriate sample size (Wang & Rhemtulla, 2021), as we consider the smaller sample size of participants as another limitation of the study.”
Method
Metadata
- Authors
- Wang, Y. A., & Rhemtulla, M.
- Venue
- Advances in Methods and Practices in Psychological Science
- Publisher
- SAGE Publications
- 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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