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Author: Anthony, James C.
Resulting in 3 citations.
1. Chung, Hwan
Anthony, James C.
A Bayesian Approach to a Multiple-Group Latent Class-Profile Analysis: The Timing of Drinking Onset and Subsequent Drinking Behaviors Among U.S. Adolescents
Structural Equation Modeling: A Multidisciplinary Journal 20,4 (2013): 658-680.
Also: http://www.tandfonline.com/doi/full/10.1080/10705511.2013.824783#.UugFYxBOlpg
Cohort(s): NLSY97
Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis
Keyword(s): Adolescent Behavior; Alcohol Use; Bayesian; Modeling, Latent Class Analysis/Latent Transition Analysis; Monte Carlo

Permission to reprint the abstract has been denied by the publisher.

Bibliography Citation
Chung, Hwan and James C. Anthony. "A Bayesian Approach to a Multiple-Group Latent Class-Profile Analysis: The Timing of Drinking Onset and Subsequent Drinking Behaviors Among U.S. Adolescents." Structural Equation Modeling: A Multidisciplinary Journal 20,4 (2013): 658-680.
2. Chung, Hwan
Anthony, James C.
Schafer, Joseph L.
Latent Class Profile Analysis: An Application to Stage Sequential Processes in Early Onset Drinking Behaviours
Journal of the Royal Statistical Society: Series A (Statistics in Society) 174,3 (July 2011): 689–712.
Also: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-985X.2010.00674.x/full
Cohort(s): NLSY97
Publisher: Wiley Online
Keyword(s): Adolescent Behavior; Alcohol Use

Permission to reprint the abstract has not been received from the publisher.

Summary: In longitudinal research on early onset drinkers, much attention has been paid to the identification of subgroups of individuals who follow similar sequential patterns of drinking behaviours. However, research on the sequential development of drinking behaviour can be challenging in part because it may not be possible to observe the particular drinking behaviour stage at a given point in time directly. To address this difficulty, we can use a latent class analysis, which provides a set of principles for the systematic identification of homogeneous subgroups of individuals. In this work, we apply a latent class analysis in an investigation of stage sequential patterns of drinking behaviours among early onset drinkers, using data from the National Longitudinal Survey of Youth 1997. A latent class analysis approach is used to sort different patterns of drinking behaviours into a small number of classes at each measurement occasion; and the class sequencing of early onset drinkers over the entire set of time points is evaluated to identify two or more homogeneous early onset drinkers who exhibit a similar sequence of class memberships over time. This approach uncovers four common drinking behaviours in early onset drinkers over three measurements from early to late adolescence. The sequences of drinking behaviours can be grouped into three sequential patterns representing the most probable progression of early onset drinking behaviours.
Bibliography Citation
Chung, Hwan, James C. Anthony and Joseph L. Schafer. "Latent Class Profile Analysis: An Application to Stage Sequential Processes in Early Onset Drinking Behaviours." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174,3 (July 2011): 689–712. A.
3. Jeon, Saebom
Seo, Tae Seok
Anthony, James C.
Chung, Hwan
Latent Class Analysis for Repeatedly Measured Multiple Latent Class Variables
Multivariate Behavioral Research published online (25 November 2020): DOI: 10.1080/00273171.2020.1848515.
Also: https://www.tandfonline.com/doi/full/10.1080/00273171.2020.1848515
Cohort(s): NLSY97
Publisher: Taylor & Francis
Keyword(s): Alcohol Use; Drug Use; Modeling, Latent Class Analysis/Latent Transition Analysis; Statistical Analysis

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.
Bibliography Citation
Jeon, Saebom, Tae Seok Seo, James C. Anthony and Hwan Chung. "Latent Class Analysis for Repeatedly Measured Multiple Latent Class Variables." Multivariate Behavioral Research published online (25 November 2020): DOI: 10.1080/00273171.2020.1848515.