title: Recovery of Weak Factor Loadings When Adding the Mean Structure in Confirmatory Factor Analysis: A Simulation Study.
abstract: This article extends previous research on the recovery of weak factor loadings in confirmatory factor analysis (CFA) by exploring the effects of adding the mean structure. This issue has not been examined in previous research. This study is based on the framework of Yung and Bentler (1999) and aims to examine the conditions that affect the recovery of weak factor loadings when the model includes the mean structure, compared to analyzing the covariance structure alone. A simulation study was conducted in which several constraints were defined for one-, two-, and three-factor models. Results show that adding the mean structure improves the recovery of weak factor loadings and reduces the asymptotic variances for the factor loadings, particularly for the models with a smaller number of factors and a small sample size. Therefore, under certain circumstances, modeling the means should be seriously considered for covariance models containing weak factor loadings.
authors.affiliation: Department of Psychology, Autonomous University of Madrid Madrid, Spain.
authors.forename: Carmen
authors.initials: C
authors.lastname: Ximénez
keywords: Monte Carlo simulation; confirmatory factor analysis; mean structure; psychometric models; recovery of weak factor loadings