Institute for Positive Psychology and Education, Australian Catholic University.
Calcul Québec, University of Sherbrooke.
Psychol Methods. 2017 Mar;22(1):166-190. doi: 10.1037/met0000084. Epub 2016 Sep 19.
This article evaluates the impact of partial or total covariate inclusion or exclusion on the class enumeration performance of growth mixture models (GMMs). Study 1 examines the effect of including an inactive covariate when the population model is specified without covariates. Study 2 examines the case in which the population model is specified with 2 covariates influencing only the class membership. Study 3 examines a population model including 2 covariates influencing the class membership and the growth factors. In all studies, we contrast the accuracy of various indicators to correctly identify the number of latent classes as a function of different design conditions (sample size, mixing ratio, invariance or noninvariance of the variance-covariance matrix, class separation, and correlations between the covariates in Studies 2 and 3) and covariate specification (exclusion, partial or total inclusion as influencing class membership, partial or total inclusion as influencing class membership, and the growth factors in a class-invariant or class-varying manner). The accuracy of the indicators shows important variation across studies, indicators, design conditions, and specification of the covariates effects. However, the results suggest that the GMM class enumeration process should be conducted without covariates, and should rely mostly on the Bayesian information criterion (BIC) and consistent Akaike information criterion (CAIC) as the most reliable indicators under conditions of high class separation (as indicated by higher entropy), versus the sample size adjusted BIC or CAIC (SBIC, SCAIC) and bootstrapped likelihood ratio test (BLRT) under conditions of low class separation (indicated by lower entropy). (PsycINFO Database Record
本文评估了部分或全部协变量的包含或排除对增长混合模型（GMM）分类枚举性能的影响。研究 1 考察了当人口模型不包含协变量而指定时包含不活跃协变量的效果。研究 2 考察了人口模型指定了 2 个仅影响类别成员的协变量的情况。研究 3 考察了一个包含 2 个协变量的人口模型，这两个协变量影响类别成员和增长因素。在所有研究中，我们对比了各种指标的准确性，以正确识别潜在类别的数量作为不同设计条件（样本大小、混合比、方差协方差矩阵的不变性或非不变性、类别分离以及协变量在研究 2 和 3 中的相关性）和协变量规范（排除、部分或全部包含作为影响类别成员、部分或全部包含作为影响类别成员，以及以类别不变或类别变化的方式影响增长因素）的函数。指标的准确性在研究之间、指标之间、设计条件之间以及协变量效应的规范方面存在重要差异。然而，结果表明，GMM 分类枚举过程应该在没有协变量的情况下进行，并且应该主要依赖于贝叶斯信息准则（BIC）和一致的 Akaike 信息准则（CAIC）作为高类别分离（由更高的熵表示）条件下最可靠的指标，而不是样本量调整的 BIC 或 CAIC（SBIC、SCAIC）和引导似然比检验（BLRT）在低类别分离（由较低的熵表示）条件下。
