Performance of Latent Growth Curve Models with Binary Variables. Issue 6 (1st November 2020) - Newsom, Jason T.; Smith, Nicholas A.
ABSTRACT: A Monte Carlo simulation examined estimation difficulties and parameter and standard error bias for mean and variance estimates of binary latent growth curve models using mean and variance adjusted diagonally weighted least squares (WLSMV) and robust maximum likelihood (MLR). Small and medium effects of slope means and variances for longitudinal designs with three, five, and seven time points and sample sizes of 100, 200, 500, and 1000 were examined. Results indicated that more time points, larger sample size, and more symmetric distributions were associated with fewer improper solutions, lower parameter and standard error bias, better Type I error rates, and better coverage. WLSMV and MLR performed acceptably with at least five time points and sample size of 500, but WLSMV performance depended on the model specification. Three time points and 100 cases appeared to be too few for accurate estimation of binary latent growth curve models for any method.