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

SUMMARY

This article addresses the approximate approach to assess measurement invariance with(longitudinal) confirmatory factor analysis. Approximate measurement invariance useszero-mean, small-variance Bayesian priors to allow minor differences in estimatedparameters across time, while still maintaining comparability of the underlying constructs.The procedure is illustrated for the first time with panel data on young peoples’ preferencesto maximize pleasure and enjoy life. Results indicate whereas the traditional approach ofexact measurement invariance failed to establish scalar invariance across time and precludedcomparisons 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 variancewas deemed sufficient to account for minor deviations of cross-time intercept differencesfrom zero.