  The current research has two aims. First, to demonstrate the utility
conditional nonparametric multilevel latent class analysis (NP-MLCA) for
multi-site program evaluation using an empirical dataset. Second, to
investigate how classification accuracy and parameter estimation of a
conditional NP-MLCA are affected by six study factors: the quality of latent
class indicators, the number of latent class indicators, level-1 covariate
effects, cross-level covariate effects, the number of level-2 units, and the
size of level-2 units. A total of 96 conditions was examined using a simulation
study. The resulting classification accuracy rates, the power and type-I error
of cross-level covariate effects and contextual effects suggest that the
nonparametric multilevel latent class model can be applied broadly in
multilevel contexts.
