This article 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 expectation-maximization 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 in 2015. The JLCA approach identifies the different joint patterns of 4 latent variables: violent behavior, alcohol consumption, tobacco cigarette smoking, and other drug use. The JLCA uncovers 4 common violent behaviors and 3 representative behavioral patterns for each of 3 other latent variables. In addition, the JLCA supports 3 common joint classes, representing the most probable simultaneous patterns for being violent and being a drug user among adolescent males.

中文翻译：

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多个离散潜在变量的潜在类别分析：暴力行为与吸毒行为之间的关联研究

本文提出了一种新型的潜在类分析，联合潜在类分析（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。关于参数的推论是通过期望最大化和 Newton-Raphson 算法的混合方法获得的。我们将 JLCA 应用于青少年暴力行为和吸毒行为的调查。数据来自 2015 年参加青少年风险行为监测系统的 4,957 名男高中生。 JLCA 方法确定了 4 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 揭示了其他 3 个潜在变量的 4 种常见暴力行为和 3 种代表性行为模式。此外，JLCA 支持 3 种常见的联合类别，代表了青春期男性中暴力和吸毒的最可能同时发生的模式。