标题： 两步潜变量模型估计量的方差-协方差矩阵估计：一种通用的基于模拟的方法 显示英文标题

标题： Estimating the variance-covariance matrix of two-step estimates of latent variable models: A general simulation-based approach

摘要： 我们提出了一种通用过程，用于估计结构参数在潜在变量模型中的两步估计量的方差-协方差矩阵。 该方法部分基于模拟，在于它包括从两步估计的第一步中获得的测量参数的抽样分布中抽取模拟值，并利用它们来量化第二步参数估计量的部分变异性。 这在渐近意义上等同于标准闭式估计的方差-协方差矩阵，但它避免了需要计算一个交叉导数矩阵，而这是标准估计中最不方便的部分。 该方法可以应用于任何类型的潜在变量模型。 我们在两个常见模型的背景下更详细地介绍了该方法，其中测量项目是分类的：具有分类潜在变量的潜在类别模型和具有连续潜在变量的潜在特质模型。 所提出的过程的良好性能通过模拟研究得到证明，并通过两个应用实例进行说明。 显示英文摘要

摘要： We propose a general procedure for estimating the variance-covariance matrix of two-step estimates of structural parameters in latent variable models. The method is partially simulation-based, in that it includes drawing simulated values of the measurement parameters of the model from their sampling distribution obtained from the first step of two-step estimation, and using them to quantify part of the variability in the parameter estimates from the second step. This is asymptotically equal with the standard closed-form estimate of the variance-covariance matrix, but it avoids the need to evaluate a cross-derivative matrix which is the most inconvenient element of the standard estimate. The method can be applied to any types of latent variable models. We present it in more detail in the context of two common models where the measurement items are categorical: latent class models with categorical latent variables and latent trait models with continuous latent variables. The good performance of the proposed procedure is demonstrated with simulation studies and illustrated with two applied examples.