Two-Step Estimation of Models Between Latent Classes and External Variables
Two-Step Estimation of Models Between Latent Classes and External Variables
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DOI：
10.1007/s11336-017-9592-7
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发表时间：
2018-12-01
期刊：
影响因子：
3
通讯作者：
Kuha, Jouni
中科院分区：
文献类型：
--
作者：
Bakk, Zsuzsa;Kuha, Jouni
我们认为模型结合了联合收割机潜在类测量模型的分类潜在变量与结构回归模型之间的关系的潜在类和观察到的解释和响应变量。我们提出了一个两步的方法来估计这样的模型。在其第一步骤中，单独估计测量模型，并且在第二步骤中，当估计结构模型时，该测量模型的参数保持固定。仿真研究和应用实例表明，两步法是一个有吸引力的替代现有的一步法和三步法。我们推导出估计的标准误差的结构模型的两步估计的不确定性，从两个步骤的估计，并显示该方法可以在现有的软件中实现的潜变量建模。
We consider models which combine latent class measurement models for categorical latent variables with structural regression models for the relationships between the latent classes and observed explanatory and response variables. We propose a two-step method of estimating such models. In its first step, the measurement model is estimated alone, and in the second step the parameters of this measurement model are held fixed when the structural model is estimated. Simulation studies and applied examples suggest that the two-step method is an attractive alternative to existing one-step and three-step methods. We derive estimated standard errors for the two-step estimates of the structural model which account for the uncertainty from both steps of the estimation, and show how the method can be implemented in existing software for latent variable modelling.