Approximate measurement invariance and longitudinal confirmatory factor analysis: concept and application with panel data

Abstract

This article addresses the approximate approach to assess measurement invariance with (longitudinal) confirmatory factor analysis. Approximate measurement invariance uses zero-mean, small-variance Bayesian priors to allow minor differences in estimated parameters across time, while still maintaining comparability of the underlying constructs. The procedure is illustrated for the first time with panel data on young peoples’ preferences to maximize pleasure and enjoy life. Results indicate whereas the traditional approach of exact measurement invariance failed to establish scalar invariance across time and precluded comparisons of latent means, it was possible to establish approximate scalar invariance. Based on a monitoring procedure for model fit and convergence, a rather small prior variance was deemed sufficient to account for minor deviations of cross-time intercept differences from zero.