Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design

This article examines whether Bayesian estimation with minimally informed prior distributions can alleviate the estimation problems often encountered with fitting the true score multitrait–multimethod structural equation model with split-ballot data. In particular, the true score multitrait–multimethod structural equation model encounters an empirical underidentification when (a) latent variable correlations are homogenous, and (b) fitted to data from a 2-group split-ballot design; an understudied case of empirical underidentification due to a planned missingness (i.e., split-ballot) design. A Monte Carlo simulation and 3 empirical examples showed that Bayesian estimation performs better than maximum likelihood (ML) estimation. Therefore, we suggest using Bayesian estimation with minimally informative prior distributions when estimating the true score multitrait–multimethod structural equation model with split-ballot data. Furthermore, given the increase in planned missingness designs in psychological research, we also suggest using Bayesian estimation as a potential alternative to ML estimation for analyses using data from planned missingness designs.