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Continuous - Linear regression (with normally distributed residuals)
Dichotomous (specified as nominal, ordinal, or a binomial count) - Binary logistic regression
Nominal (with more than 2 levels) - Multinomial logistic regression
Ordinal (with more than 2 ordered levels) -
Adjacent-category ordinal logistic regression
Count: Log-linear Poisson regression
Binomial Count: Binomial logistic regression model
Step3模块
执行潜在类分析后，您可能希望调查类成员与外部变量之间的关系。一种流行的三步法是首先估计感兴趣的潜在类别模型（步骤1），然后使用他们的后验类别属性概率将个体分配给潜在类别（步骤2），然后调查的类成员与外部变量之间的关联（第3步）。
在步骤2中，在将个体分配给潜在类别时会引入分类错误。与外部变量关联的估计值需要针对分类错误进行修正，以防止向下偏差（Bolck，Croon和Hagenaars，2004）。Step3模块执行两个偏差调整程序（Vermunt，2010）。
Step3模块可以与预测类成员关系的外部变量（协变量选项）一起使用，也可以与类成员关系预测的外部变量（依赖选项）一起使用。这两种类型的外部变量也分别称为伴随变量和远端结果。
您还可以选择使用模态或比例分配规则将案例分配给潜在类，并获得用于评分新案例的方程式。
Overview
Latent GOLD 5.0 is a powerful latent class and finite mixture program. Latent GOLD contains separate modules for estimating three different model structures:
Latent Class Cluster models
Discrete Factor (DFactor) models
Latent Class Regression models
After performing a latent class analysis, you might wish to investigate the relationship between class membership and external variables. A popular three-step approach is to first estimate the latent class model of interest (step 1), then assign individuals to latent classes using their posterior class membership probabilities (step 2), and subsequently investigate the association between the assigned class memberships and external variables (step 3).
"This program by Jay Magidson and Jeroen Vermunt is simply splendid! With great graphics, and intuitive commands, it includes new, state-of-the-art technical features like Bayes constants to help avoid boudary solutions, methods to diagnose and relax local dependence, and automatic testing of multiple start values. The features are too many to list..." John Uebersax.
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