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The current research has two aims. First, to demonstrate the utilityconditional nonparametric multilevel latent class analysis (NP-MLCA) formulti-site program evaluation using an empirical dataset. Second, toinvestigate how classification accuracy and parameter estimation of aconditional NP-MLCA are affected by six study factors: the quality of latentclass indicators, the number of latent class indicators, level-1 covariateeffects, cross-level covariate effects, the number of level-2 units, and thesize of level-2 units. A total of 96 conditions was examined using a simulationstudy. The resulting classification accuracy rates, the power and type-I errorof cross-level covariate effects and contextual effects suggest that thenonparametric multilevel latent class model can be applied broadly inmultilevel contexts.

Classification Accuracy and Parameter Estimation in Multilevel Contexts: A Study of Conditional Nonparametric Multilevel Latent Class Analysis