The Impact of Small Cluster Size on Multilevel Models: A Monte Carlo Examination of Two-Level Models with Binary and Continuous Predictors

Recent methodological research has addressed the important issue of sample size at each level when estimating multilevel models. Although several design factors have been investigated in these studies, differences between continuous and binary predictor variables have not been scrutinized (previous findings are based on models with continuous predictor variables). To help address this gap in the literature, this Monte Carlo study focused on the consequences of level-2 sparseness on the estimation of fixed and random effects coefficients in terms of model convergence and both point and interval parameter estimates. The 5,760 conditions simulated in the Monte Carlo study varied in terms of level-1 sample size, number of level-2 units, proportion of singletons (level-2 units with one observation), type of predictor, collinearity, intraclass correlation, and model complexity.