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

TL;DR

This study demonstrates the utility of conditional nonparametric multilevel latent class analysis (NP-MLCA) for multi-site program evaluation and investigates how various study factors influence its accuracy and parameter estimation through simulation.

Contribution

It introduces the application of NP-MLCA in multilevel contexts and examines the impact of six study factors on its performance using extensive simulations.

Findings

NP-MLCA provides accurate classification in multilevel settings.

Study factors significantly affect classification accuracy and parameter estimates.

The method is broadly applicable in multilevel research contexts.

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.