McNeish et al. argue for the general use of covariance pattern growth mixture models because these models do not involve the assumption of random effects, demonstrate high rates of convergence, and are most likely to identify the correct number of latent subgroups. We argue that the covariance pattern growth mixture model is a single random intercept model. It and other models considered in their article are special cases of a general model involving slope and intercept factors. We argue growth mixture models are multigroup invariance hypotheses based on unknown subgroups. Psychometric models in which trajectories are modeled using slope factor loadings which vary by latent subgroup are often conceptually preferable. Convergence rates for mixture models can be substantially improved by using a variance component start value taken from analyses with one fewer class and by specifying multifactor models in orthogonal form. No single latent growth model is appropriate across all research contexts and, instead, the most appropriate latent mixture model must be "right-sized" to the data under consideration. Reanalysis of a real-world longitudinal data set of posttraumatic stress disorder symptomatology reveals a three-group model involving exponential decline, further suggesting that the four-group "cat's cradle" pattern frequently reported is artefactual. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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潜在增长混合模型作为潜在变量多组因子模型：对 McNeish 等人的评论。 （2023）。


麦克尼什等人。主张普遍使用协方差模式增长混合模型，因为这些模型不涉及随机效应的假设，表现出高收敛率，并且最有可能识别出正确数量的潜在子组。我们认为协方差模式增长混合模型是单一随机截距模型。它和他们文章中考虑的其他模型是涉及斜率和截距因子的一般模型的特殊情况。我们认为增长混合模型是基于未知子组的多组不变性假设。使用随潜在子组变化的斜率因子载荷对轨迹进行建模的心理测量模型在概念上通常是更可取的。通过使用从少一类的分析中获取的方差分量起始值以及以正交形式指定多因子模型，可以显着提高混合模型的收敛率。没有单一的潜在增长模型适用于所有研究背景，相反，最合适的潜在混合模型必须与所考虑的数据“大小合适”。对创伤后应激障碍症状学真实世界纵向数据集的重新分析揭示了涉及指数下降的三组模型，进一步表明经常报道的四组“猫的摇篮”模式是人为的。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。