Search

Research on stage-sequential shifts across multiple latent classes can be challenging in part because it may not be possible to observe the particular stage-sequential pattern of a single latent class variable directly. In addition, one latent class variable may affect or be affected by other latent class variables and the associations among multiple latent class variables are not likely to be directly observed either. To address this difficulty, we propose a multivariate latent class analysis for longitudinal data, joint latent class profile analysis (JLCPA), which provides a principle for the systematic identification of not only associations among multiple discrete latent variables but sequential patterns of those associations. We also propose the recursive formula to the EM algorithm to overcome the computational burden in estimating the model parameters, and our simulation study shows that the proposed algorithm is much faster in computing estimates than the standard EM method. In this work, we apply a JLCPA using data from the National Longitudinal Survey of Youth 1997 in order to investigate the multiple drug-taking behavior of early-onset drinkers from their adolescence, via young adulthood, to adulthood. [ABSTRACT FROM AUTHOR]

In this paper we address two issues in the use of the nonresponse log-linear models for the analysis of an incomplete two-way contingency table with one variable subject to nonresponse, the occurrence of nonresponse boundary solutions and the assessment of the missing data mechanism. To this end, we employ a set of response odds from the fully classified counts and nonresponse odds from partially classified counts. We first investigate the role of the set of odds in identifying the occurrence of boundary solutions and assessing the nonresponse log-linear models suitable for the data, and then propose a data analytic guideline for the analysis of an incomplete two-way contingency table. We also examine the theoretical properties of the set of odds when nonresponse boundary solutions occur. [ABSTRACT FROM AUTHOR]

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.