Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration
Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration
复制标题
DOI：
10.1080/00273171.2019.1593813
复制
发表时间：
2019-04-19
影响因子：
3.8
通讯作者：
Guerra-Pena, Kiero
中科院分区：
文献类型：
--
作者：
Depaoli, Sarah;Winter, Sonja D.;Guerra-Pena, Kiero
最近的进展允许在潜在增长建模中使用稳健的偏态混合分布而不是正态分布来建模混合成分。此功能通过直接对倾斜或重尾潜在类进行建模，而不是假设正态分布的混合，增加了处理纵向数据中的非正态性的灵活性。本研究的目的是通过模拟潜在的下或过度提取的潜在类的增长混合模型时，底层数据遵循正态分布，偏态正态分布，或偏态t分布进行评估。为了评估这一点，我们实现了skewed-t，skewed-normal和常规正态（即，不偏斜的）增长混合模型的形式。该模型的斜t和斜正态版本最近才被实现，并且对它们的性能知之甚少。通过各种指标评估模型比较、拟合和正确指定和错误指定模型的分类。研究结果表明，模型比较和拟合测量的准确性取决于（错误）规范的类型，以及潜在类之间的类分离量。二次模拟暴露了在一些倾斜的建模环境下的计算和准确性困难。讨论了研究结果的影响、对应用研究人员的建议以及未来的方向;使用教育数据提供了一个鼓舞人心的例子。
Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulation the potential under- or over-extraction of latent classes in a growth mixture model when underlying data follow either normal, skewed-normal, or skewed-t distributions. In order to assess this, we implement skewed-t, skewed-normal, and conventional normal (i.e., not skewed) forms of the growth mixture model. The skewed-t and skewed-normal versions of this model have only recently been implemented, and relatively little is known about their performance. Model comparison, fit, and classification of correctly specified and mis-specified models were assessed through various indices. Findings suggest that the accuracy of model comparison and fit measures are dependent on the type of (mis)specification, as well as the amount of class separation between the latent classes. A secondary simulation exposed computation and accuracy difficulties under some skewed modeling contexts. Implications of findings, recommendations for applied researchers, and future directions are discussed; a motivating example is presented using education data.