Why and How to Use Factor Scores in Multiple Imputation to Improve Estimation of Longitudinal Models Under Simultaneous Missingness across Items

We propose a multiple imputation (MI) approach with factor scores to improve the quality and robustness of longitudinal inferential results under simultaneous missingness (all items in the scale are missing when one is missing). A Monte Carlo simulation study and an empirical example based on affect data are presented.