Latent Class Modeling with Covariates: Two Improved Three-Step Approaches
Latent Class Modeling with Covariates: Two Improved Three-Step Approaches
复制标题
带有协变量的潜在类建模： 两种改进的三步法
DOI：
10.1093/pan/mpq025
复制
发表时间：
2010-09-01
影响因子：
5.4
通讯作者：
Vermunt, Jeroen K.
中科院分区：
文献类型：
--
作者：
Vermunt, Jeroen K.
使用潜在类别 (LC) 分析的研究人员通常采用以下三个步骤进行：(1) 为一组响应变量构建 LC 模型，(2) 根据受试者的后验类别成员概率将受试者分配到 LC，(3) 使用简单的交叉表或多项逻辑回归分析来研究分配的类别成员与外部变量之间的关联。 Bolck、Croon 和 Hagenaars（2004）证明，这种三步法低估了协变量和类别成员之间的关联。他们提出通过修改第三步的具体校正方法来解决这个问题。在本文中，我扩展了 Bolck、Croon 和 Hagenaars 的校正方法，表明它涉及最大化聚类数据的加权对数似然函数。这种概念化使得该方法不仅可以应用于分类变量，还可以应用于连续解释变量，使用复杂的抽样方差估计方法获得正确的测试，并在逻辑回归分析的标准软件中实现它。此外，还提出了一种新的基于最大似然（ML）的校正方法，该方法更直接，不需要分析加权数据。这种新的三步 ML 方法可以在 LC 分析软件中轻松实现。所报告的模拟研究表明，两种校正方法都表现得非常好，因为它们的参数估计和 SE 是可信的，除了类分离非常差的情况之外。与 Bolck、Croon 和 Hagenaars 方法相比，ML 方法的主要优点是效率更高，几乎与一步 ML 估计一样高效。
Researchers using latent class (LC) analysis often proceed using the following three steps: (1) an LC model is built for a set of response variables, (2) subjects are assigned to LCs based on their posterior class membership probabilities, and (3) the association between the assigned class membership and external variables is investigated using simple cross-tabulations or multinomial logistic regression analysis. Bolck, Croon, and Hagenaars (2004) demonstrated that such a three-step approach underestimates the associations between covariates and class membership. They proposed resolving this problem by means of a specific correction method that involves modifying the third step. In this article, I extend the correction method of Bolck, Croon, and Hagenaars by showing that it involves maximizing a weighted log-likelihood function for clustered data. This conceptualization makes it possible to apply the method not only with categorical but also with continuous explanatory variables, to obtain correct tests using complex sampling variance estimation methods, and to implement it in standard software for logistic regression analysis. In addition, a new maximum likelihood (ML)-based correction method is proposed, which is more direct in the sense that it does not require analyzing weighted data. This new three-step ML method can be easily implemented in software for LC analysis. The reported simulation study shows that both correction methods perform very well in the sense that their parameter estimates and their SEs can be trusted, except for situations with very poorly separated classes. The main advantage of the ML method compared with the Bolck, Croon, and Hagenaars approach is that it is much more efficient and almost as efficient as one-step ML estimation.