Jeon Saebom, Lee Jungwun, Anthony James C, Chung Hwan
Department of Marketing Information Consulting, Mokwon University.
Department of Statistics, Korea University.
Struct Equ Modeling. 2017;24(6):911-925. doi: 10.1080/10705511.2017.1340844. Epub 2017 Jul 12.
This paper proposes a new type of latent class analysis, joint latent class analysis (JLCA), which provides a set of principles for the systematic identification of the subsets of joint patterns for multiple discrete latent variables. Inferences about the parameters are obtained by a hybrid method of EM and Newton-Raphson algorithms. We apply JLCA in an investigation of adolescent violent behavior and drug-using behaviors. The data are from 4,957 male high-school students who participated in the Youth Risk Behavior Surveillance System 2015. The JLCA approach identifies the different joint patterns of four latent variables: violent behavior, alcohol consumption, tobacco cigarette smoking, and other drug use. The JLCA uncovers four common violent behaviors and three representative behavioral patterns for each of three other latent variables. In addition, the JLCA supports three common joint classes, representing the most probable simultaneous patterns for being violent and being a drug user among adolescent males.
本文提出了一种新型的潜在类别分析方法，即联合潜在类别分析（JLCA），它为系统识别多个离散潜在变量的联合模式子集提供了一套原则。通过期望最大化（EM）算法和牛顿 - 拉夫森算法的混合方法来获得参数推断。我们将JLCA应用于青少年暴力行为和吸毒行为的调查中。数据来自4957名参与2015年青少年风险行为监测系统的男性高中生。JLCA方法识别出四个潜在变量的不同联合模式：暴力行为、饮酒、吸烟和其他药物使用。JLCA揭示了四种常见的暴力行为以及其他三个潜在变量各自的三种代表性行为模式。此外，JLCA支持三种常见的联合类别，代表了青少年男性中暴力行为和吸毒行为最可能同时出现的模式。
