Open AccessPower Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial
Author(s) -
Yilin Andre Wang,
Mijke Rhemtulla
Publication year - 2021
Publication title -
advances in methods and practices in psychological science
Language(s) - English
Resource type - Journals
eISSN - 2515-2467
pISSN - 2515-2459
DOI - 10.1177/2515245920918253
Subject(s) - structural equation modeling , regression analysis , sample (material) , latent variable , power (physics) , statistics , sample size determination , rule of thumb , computer science , econometrics , power analysis , popularity , linear regression , regression , data mining , mathematics , algorithm , psychology , social psychology , chemistry , physics , chromatography , quantum mechanics , cryptography
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.