Extracting Spurious Latent Classes in Growth Mixture Modeling With Nonnormal Errors
Extracting Spurious Latent Classes in Growth Mixture Modeling With Nonnormal Errors
复制标题
DOI：
10.1177/0013164416633735
复制
发表时间：
2016-12-01
影响因子：
2.7
通讯作者：
Steinley, Douglas
中科院分区：
文献类型：
--
作者：
Guerra-Pena, Kiero;Steinley, Douglas
生长混合模型通常用于两个目的：（1）识别正态子群的混合和（2）通过正态分量的混合来近似奇怪形状的分布。通常在应用研究中，这种方法被模糊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。这可能导致潜在类的过度延伸，并将实质意义归于这些虚假类。本研究的目的是（1）探索贝叶斯信息准则，样本调整BIC和自助似然比检验在具有非正态分布结果变量的生长混合模型分析中的性能，以及（2）当结果变量呈正态分布时，检查非正态时不变协变量在估计潜在类数时的影响。对于这两个目标，我们将包括非正常的情况下，没有考虑以前的文献。进行了两次模拟研究。结果表明，虚假的类可能会选择和最佳的解决方案，在数据分析时，人口偏离正态性，即使非正态性只存在于时不变的协变量。
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.