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arXiv · 2010.15368

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

Chi Chang·Kimberly Kelly·M. Lee Van Horn·Richard T. Houang·Joseph Gardiner·Laurie Van Egeren·Heng-Chieh Wu
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Abstract

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.

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Chi Chang, Kimberly Kelly, M. Lee Van Horn, Richard T. Houang, Joseph Gardiner, Laurie Van Egeren, Heng-Chieh Wu. 2020-10-29. Classification Accuracy and Parameter Estimation in Multilevel Contexts: A Study of Conditional Nonparametric Multilevel Latent Class Analysis. https://arxiv.org/abs/2010.15368

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