The Impact of MNAR Attrition on Estimation of Latent Growth Curve Models with Binary Observed Variables

A Monte Carlo simulation was used to evaluate the performance of latent growth curve (LGC) models with binary observed variables when the attrition pattern was missing not at random (MNAR) for five time points. Parameter and standard error biases for three estimation methods were compared: weighted least squares with mean and variance adjustment (WLSMV), categorical robust marginal maximum likelihood (categorical MLR), and Bayes. The results indicated that robust diagonal weighted least squares paired with multiple imputation (MI) performed best when values were missing at random. When data were missing not at random, Bayesian estimates performed best.