The consequences of ignoring measurement invariance for path coefficients in structural equation models.

ABSTRACT: We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance - modeling and ignoring non-invariant items - on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance - non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds - in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direc

SUBMITTER: Guenole N 

PROVIDER: S-EPMC4166111 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature