Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes.
Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes.
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DOI：
10.3389/fpsyg.2017.01499
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发表时间：
2017
影响因子：
3.8
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Kim ES;Wang Y
生长轨迹中的群体异质性可以通过生长混合模型（GMM）来检测。研究人员通常会计算重复测量的综合分数，并假设潜在类别之间的测量不变性，将其用作生长因子（基线表现和生长）的多个指标。考虑到测量不变性的假设并不总是成立，我们通过蒙特卡洛模拟研究（研究1）研究了测量不变性对GMM中类枚举和参数恢复的影响。在研究 2 中，我们研究了二阶增长混合模型 (SOGMM) 的类枚举和参数恢复，该模型结合了一阶测量模型。因此，SOGMM 使用可靠的方差来源（即重复测量的公因子方差）估计生长轨迹参数，并允许潜在类别之间的测量参数存在异质性。在各种模拟条件下，使用 AIC、BIC、样本量调整的 BIC 和分层 BIC 等信息标准来检查类别枚举率。研究 1 的结果表明，即使提取了正确数量的潜在类别，基线性能和生长因子均值的参数估计也存在测量非不变性程度的偏差。在研究2中，SOGMM的类枚举精度取决于信息标准、类分离和样本量。类别之间的基线表现和生长因子平均差异的估计通常是无偏的，但测量非不变性的大小被低估。总体而言，SOGMM 的优势在于，与 GMM 相比，通过合并测量模型，它可以产生增长轨迹参数的无偏估计和更准确的类别枚举。
Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance and growth) assuming measurement invariance between latent classes. Considering that the assumption of measurement invariance does not always hold, we investigate the impact of measurement noninvariance on class enumeration and parameter recovery in GMM through a Monte Carlo simulation study (Study 1). In Study 2, we examine the class enumeration and parameter recovery of the second-order growth mixture modeling (SOGMM) that incorporates measurement models at the first order level. Thus, SOGMM estimates growth trajectory parameters with reliable sources of variance, that is, common factor variance of repeated measures and allows heterogeneity in measurement parameters between latent classes. The class enumeration rates are examined with information criteria such as AIC, BIC, sample-size adjusted BIC, and hierarchical BIC under various simulation conditions. The results of Study 1 showed that the parameter estimates of baseline performance and growth factor means were biased to the degree of measurement noninvariance even when the correct number of latent classes was extracted. In Study 2, the class enumeration accuracy of SOGMM depended on information criteria, class separation, and sample size. The estimates of baseline performance and growth factor mean differences between classes were generally unbiased but the size of measurement noninvariance was underestimated. Overall, SOGMM is advantageous in that it yields unbiased estimates of growth trajectory parameters and more accurate class enumeration compared to GMM by incorporating measurement models.
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