Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation, though this may be a dubious assumption. Alternative model specifications have been proposed to reduce nonconvergence without imposing covariance equality constraints. These methods perform well when the correct number of classes is known, but research has not yet examined their use when the number of classes is unknown. Given the importance of selecting the number of classes, more information about class enumeration performance is crucial to assess the potential utility of these methods. We conducted an extensive simulation to explore class enumeration and classification accuracy of model specifications that are more robust to nonconvergence. Results show that the typical approach of applying covariance equality constraints performs quite poorly. Instead, we recommended covariance pattern GMMs because they (a) had the highest convergence rates, (b) were most likely to identify the correct number of classes, and (c) had the highest classification accuracy in many conditions, even with modest sample sizes. An analysis of empirical posttraumatic stress disorder (PTSD) data is provided to show that the typical four-class solution found in many empirical PTSD studies may be an artifact of the covariance equality constraint method that has permeated this literature.

中文翻译：

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增长混合模型中的不收敛、协方差约束和类枚举。

生长混合模型 (GMM) 是识别潜在生长轨迹类别的流行方法。GMM 的一个缺点是不收敛，这常常导致研究人员应用协方差等式约束来简化估计，尽管这可能是一个可疑的假设。已经提出了替代模型规范来减少不收敛，而不施加协方差等式约束。当已知正确的类数时，这些方法表现良好，但研究尚未检验它们在类数未知时的使用。鉴于选择类数量的重要性，有关类枚举性能的更多信息对于评估这些方法的潜在效用至关重要。我们进行了广泛的模拟，以探索对不收敛更稳健的模型规范的类枚举和分类准确性。结果表明，应用协方差等式约束的典型方法表现很差。相反，我们推荐协方差模式 GMM，因为它们 (a) 具有最高的收敛率，(b) 最有可能识别正确的类数，并且 (c) 在许多条件下具有最高的分类精度，即使样本量适中。对经验性创伤后应激障碍 (PTSD) 数据的分析表明，许多经验性 PTSD 研究中发现的典型四类解决方案可能是该文献中渗透的协方差等式约束方法的产物。