论文标题
多级环境中的分类精度和参数估计：有条件非参数潜在类别分析的研究
Classification Accuracy and Parameter Estimation in Multilevel Contexts: A Study of Conditional Nonparametric Multilevel Latent Class Analysis
论文作者
论文摘要
当前的研究有两个目标。首先，为了证明使用经验数据集的多站点程序评估的实用性非参数潜在类别分析（NP-MLCA）。其次，为了研究条件NP-MLCA的分类准确性和参数估计如何受六个研究因素的影响：潜在类指标的质量，潜在类指标的数量，1级协变量效应，跨级协变量效应，级别-2级单位的数量以及级别2单元的大小。使用模拟研究总共检查了96个条件。所得的分类精度率，跨级协变量效应的功率和I型误差以及上下文效应表明，非参数多级潜在类模型可以广泛应用于多层次上下文。
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.
