Latent Class Analysis for Multiple Discrete Latent Variables: A Study on the Association Between Violent Behavior and Drug-Using Behaviors
- Authors
- Jeon, Saebom; Lee, Jungwun; Anthony, James C.; Chung, Hwan
- Issue Date
- 2017
- Publisher
- ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
- Keywords
- drug-using behavior; joint patterns of multiple latent variables; latent class analysis; violent behavior
- Citation
- STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, v.24, no.6, pp.911 - 925
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
- Volume
- 24
- Number
- 6
- Start Page
- 911
- End Page
- 925
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/86485
- DOI
- 10.1080/10705511.2017.1340844
- ISSN
- 1070-5511
- 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.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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