What question did this study set out to answer?

To address sample size selection challenges in structural equation modeling by differentiating power to detect overall model misspecification from power to detect specific target parameter effects.

January 1, 2021Advances in Methods and Practices in Psychological ScienceOpen Access

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

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To address sample size selection challenges in structural equation modeling by differentiating power to detect overall model misspecification from power to detect specific target parameter effects.
Conducted a Monte Carlo simulation study to evaluate statistical power for detecting a target regression coefficient in a three-predictor latent regression model.
Developed and introduced pwrSEM, an interactive R Shiny application designed to compute power for specific target effects in structural equation models.
Demonstrated that common rules of thumb and omnibus tests of model fit do not ensure sufficient statistical power for detecting specific latent regression coefficients.
Established that statistical power varies substantially based on parameter size, latent indicator reliability, and model complexity, requiring model-specific sample size planning.

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Implication

Simulation study demonstrates sample size requirements for target effects in structural equation modeling, highlighting the utility of customized power analyses over arbitrary rules of thumb.

Cite This Study

Wang et al. (2021) studied this question.

synapsesocial.com/papers/69d80c30617ce96c42ae29eb https://doi.org/10.1177/2515245920918253

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Implication

Simulation study demonstrates sample size requirements for target effects in structural equation modeling, highlighting the utility of customized power analyses over arbitrary rules of thumb.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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