Latent Class Analysis for Multiple Discrete Latent Variables: A Study on the Association Between Violent Behavior and Drug-Using Behaviors. Issue 6 (2nd November 2017)

• Record Type: Journal Article
• Authors: Jeon, Saebom; Lee, Jungwun; Anthony, James C.; Chung, Hwan
• Is Part Of: Structural equation modeling. Volume 24:Issue 6(2017)
• Page Start: 911
• Page End: 925
• Publication Date: 2017-11-02
• Subjects: drug-using behavior -- joint patterns of multiple latent variables -- latent class analysis -- violent behavior; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1340844
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : 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.