An alternative to post hoc model modification in confirmatory factor analysis: The Bayesian lasso.

ABSTRACT: As a commonly used tool for operationalizing measurement models, confirmatory factor analysis (CFA) requires strong assumptions that can lead to a poor fit of the model to real data. The post hoc modification model approach attempts to improve CFA fit through the use of modification indexes for identifying significant correlated residual error terms. We analyzed a 28-item emotion measure collected for n = 175 participants. The post hoc modification approach indicated that 90 item-pair errors were significantly correlated, which demonstrated the challenge in using a modification index, as the error terms must be individually modified as a sequence. Additionally, the post hoc modification approach cannot guarantee a positive definite covariance matrix for the error terms. We propose a method

SUBMITTER: Pan J 

PROVIDER: S-EPMC5745070 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature