SENSITIVITY ANALYSIS OF STRUCTURAL PARAMETERS TO MEASUREMENT NONINVARIANCE: A BAYESIAN APPROACH

Although some researchers have supported the concept of partial invariance, there is still no clear-cut partial invariance level which is needed to make valid group comparisons. In addition, relatively little attention has been paid to the implications of failing measurement invariance (e.g., partial measurement invariance) on group comparison on the underlying latent constructs in the multiple-group confirmatory factor analysis (MGCFA) framework.

Given this, the purpose of the current study was to examine the extent to which measurement noninvariance affects structural parameter comparisons across populations in the MGCFA framework. Particularly, this study takes a Bayesian approach to investigate the sensitivity of the posterior distribution of structural parameter difference to varying types and magnitudes of noninvariance across two populations. A Monte Carlo simulation was performed to empirically investigate the sensitivity of structural parameters to varying types and magnitudes of noninvariant measurement models across two populations from a Bayesian approach.