Evaluating fit indices in a multilevel latent growth curve model: A Monte Carlo study.

The multilevel latent growth curve model (MLGCM), which is subsumed by the multilevel structural equation modeling framework, has been advocated as a means of investigating individual and cluster trajectories. Still, how to evaluate the goodness of fit of MLGCMs has not been well addressed. The purpose of this study was to conduct a systematic Monte Carlo simulation to carefully investigate the effectiveness of (a) level-specific fit indices and (b) target-specific fit indices in an MLGCM, in terms of their independence from the sample size's influence and their sensitivity to misspecification in the MLGCM that occurs in either the between-covariance, between-mean, or within-covariance structure.

Bias and precision of some classical ANOVA effect sizes when assumptions are violated.

Previous simulation research has focused on evaluating the impact of analytic assumption violations on statistics related to the F test and associated p CALCULATED values. The present article evaluated the bias of classical estimates of practical significance (i.e.

Spiritual integration predicts self-reported mental and physical health.

Data from 167 participants were used to establish the psychometric properties of the Reidhead spiritual integration scale, 31-item version (SI-31). Structural equation modeling was used to empirically evaluate influences on perceived health functioning, while accounting for possible confounds. The analyses showed that SI-31 predicted perceived mental and physical health while controlling for life satisfaction, religious variables, mood patterns, depression symptoms, and demographics.