Lanza Stephanie T, Coffman Donna L, Xu Shu
The Methodology Center, The Pennsylvania State University ; The College of Health and Human Development, The Pennsylvania State University.
The Methodology Center, The Pennsylvania State University.
Struct Equ Modeling. 2013 Jul;20(3):361-383. doi: 10.1080/10705511.2013.797816.
The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of latent class membership. In the present article, two propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix.
现代因果推断方法与潜在类别分析(LCA)的整合,使社会、行为和健康领域的研究人员能够解决有关潜在类别成员决定因素的重要问题。在本文中,展示了两种倾向得分技术——匹配和逆倾向加权,用于在潜在类别分析中进行因果推断。仔细阐述了这些技术能够解决的不同因果问题。本文基于1979年全国青年纵向调查的数据进行了实证分析,其中将大学入学情况作为暴露(即处理)变量,并估计其对成人物质使用潜在类别成员身份的因果效应。还包括了在潜在类别分析中进行因果推断的逐步程序,包括对混杂因素、暴露变量和多变量结果的缺失数据进行多重插补。附录中给出了使用SAS和R进行分析的示例语法。