PROC LCA：一种用于潜在类别分析的SAS程序。
PROC LCA: A SAS Procedure for Latent Class Analysis.
作者信息
Lanza Stephanie T, Collins Linda M, Lemmon David R, Schafer Joseph L
机构信息
The Methodology Center, The Pennsylvania State University.
出版信息
Struct Equ Modeling. 2007;14(4):671-694. doi: 10.1080/10705510701575602.
Abstract
Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.
摘要
潜在类别分析（LCA）是一种统计方法，用于根据个体对一组观察到的分类变量的反应，识别出一组离散、相互排斥的潜在个体类别。在多组LCA中，模型的测量部分和结构部分在不同组之间可能会有所不同，并且可以通过实证检验不同组之间的测量不变性。带有协变量的LCA扩展了模型，以纳入类别归属的预测变量。在本文中，我们介绍了PROC LCA，这是一个用于进行LCA、多组LCA和带有协变量的LCA的新SAS程序。该程序使用全国高中生样本中的酒精使用行为数据进行了演示。
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