Park Jungkyu, Yu Hsiu-Ting
McGill University, Montreal, Quebec, Canada.
National Chengchi University, Taipei, Taiwan.
Educ Psychol Meas. 2016 Oct;76(5):824-847. doi: 10.1177/0013164415618240. Epub 2015 Nov 26.
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In the present study, a simulation study was conducted to investigate the impact of ignoring the higher-level nesting structure. Three criteria-the model selection accuracy, the classification quality, and the parameter estimation accuracy-were used to evaluate the impact of ignoring the nested data structure. The results of the simulation study showed that ignoring higher-level nesting structure in an MLCM resulted in the poor performance of the Bayesian information criterion to recover the true latent structure, the inaccurate classification of individuals into latent classes, and the inflation of standard errors for parameter estimates, while the parameter estimates were not biased. This article concludes with remarks on ignoring the nested structure in nonparametric MLCMs, as well as recommendations for applied researchers when LCM is used for data collected from a multilevel nested structure.
多级潜在类别模型（MLCM）是潜在类别模型（LCM）的多级扩展，用于分析嵌套结构数据结构。MLCM的非参数版本假设在较高级别的嵌套结构中有一个离散潜在变量，以解释嵌套在较高级别单元内的观测值之间的依赖性。在本研究中，进行了一项模拟研究，以调查忽略较高级别嵌套结构的影响。使用三个标准——模型选择准确性、分类质量和参数估计准确性——来评估忽略嵌套数据结构的影响。模拟研究结果表明，在MLCM中忽略较高级别嵌套结构会导致贝叶斯信息准则在恢复真实潜在结构方面表现不佳、将个体不准确地分类到潜在类别中以及参数估计的标准误差膨胀，而参数估计没有偏差。本文最后对在非参数MLCM中忽略嵌套结构进行了评论，并为应用研究人员在将LCM用于从多级嵌套结构收集的数据时提供了建议。
