Guerra-Peña Kiero, Steinley Douglas
Pontificia Universidad Católica Madre y Maestra, Santiago, Dominican Republic.
University of Missouri-Columbia, MO, USA.
Educ Psychol Meas. 2016 Dec;76(6):933-953. doi: 10.1177/0013164416633735. Epub 2016 Mar 1.
Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This can lead to the overextraction of latent classes and the attribution of substantive meaning to these spurious classes. The goals of this study are (1) to explore the performance of the Bayesian information criterion, sample-adjusted BIC, and bootstrap likelihood ratio test in growth mixture modeling analysis with nonnormal distributed outcome variables and (2) to examine the effects of nonnormal time invariant covariates in the estimation of the number of latent classes when outcome variables are normally distributed. For both of these goals, we will include nonnormal conditions not considered previously in the literature. Two simulation studies were conducted. Results show that spurious classes may be selected and optimal solutions obtained in the data analysis when the population departs from normality even when the nonnormality is only present in time invariant covariates.
（1）识别正态子组的混合；（2）通过正态成分的混合来近似形状奇特的分布。在应用研究中，这种方法常常被不加区分地应用于这两种情况：使用相同的拟合统计量和似然比检验。这可能导致潜在类别的过度提取以及将实质性意义赋予这些虚假类别。本研究的目标是：（1）在具有非正态分布结果变量的增长混合模型分析中，探索贝叶斯信息准则、样本调整后的贝叶斯信息准则和自助似然比检验的性能；（2）当结果变量呈正态分布时，检验非正态时间不变协变量对潜在类别数量估计的影响。对于这两个目标，我们将纳入文献中先前未考虑的非正态条件。进行了两项模拟研究。结果表明，即使总体偏离正态性，且非正态性仅存在于时间不变协变量中，在数据分析中仍可能选择虚假类别并获得最优解。
