The use of multilevel modeling with data from population-based^{ }surveys is often limited by the small number of cases per Level^{ }2 unit, prompting a recent trend in the neighborhood literature^{ }to apply cluster techniques to address the problem of data sparseness.^{ }In this study, the authors use Monte Carlo simulations to investigate^{ }the effects of marginal group sizes on multilevel model performance,^{ }bias, and efficiency. They then employ cluster analysis techniques^{ }to minimize data sparseness and examine the consequences in^{ }the simulations. They find that estimates of the fixed effects^{ }are robust at the extremes of data sparseness, while cluster^{ }analysis is an effective strategy to increase group size and^{ }prevent the overestimation of variance components. However,researchers should be cautious about the degree to which they^{ }use such clustering techniques due to the introduction of artificial^{ }within-group heterogeneity.

**Key Words:** multilevel models • data sparseness • cluster analysis • Monte Carlo simulations • survey research