JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Multiple-Group Latent Transition Model for the Analysis of Sequential Patterns of Early-Onset Drinking Behaviors among U.S. Adolescents
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Multiple-Group Latent Transition Model for the Analysis of Sequential Patterns of Early-Onset Drinking Behaviors among U.S. Adolescents
Chung, Hwan;
  PDF(new window)
 Abstract
We investigate the latent stage-sequential patterns of drinking behaviors of U.S. adolescents who have started to drink by age 14 years (seven years before the legal drinking age). A multiple-group latent transition analysis(LTA) with logistic regression is employed to identify the subsequent patterns of drinking behaviors among early-onset drinkers. A sample of 1407 early-onset adolescents from the National Longitudinal Survey of Youth(NLSY97) is analyzed using maximum-likelihood estimation. The analysis demonstrates that early-onset adolescents' drinking behaviors can be represented by four latent classes and their prevalence and transition are influenced by demographic factors of gender, age, and race.
 Keywords
Latent stage-sequential process;latent transition analysis;maximum likelihood;under-age drinking;
 Language
English
 Cited by
 References
1.
Agresti, A. (2002). Categorical Data Analysis, (Second ed.). Hoboken, Wiley, New Jersey.

2.
Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L. and Rathouz, P. J. (1997). Latent variable regression for multiple discrete outcomes, Journal of the American Statistical Association, 92, 1375-1386. crossref(new window)

3.
Centers for Disease Control and Prevention (2010). Youth risk behavior surveillance - United States, 2009, Surveillance Summaries, MMWR, 59 (SS-5).

4.
Chung, H., Anthony, J. C. and Schafer, J. L. (2011). Latent class profile analysis: an application to stagesequential process of under-age drinking behaviours, Journal of the Royal Statistical Society, Series A, 174.

5.
Clogg, C. C. and Goodman, L. A. (1984). Latent structure analysis of a set of multidimensional contingency tables, Journal of the American Statistical Association, 79, 762-771. crossref(new window)

6.
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via em algorithm (with discussion), Journal of the Royal Statistical Society, Series B, 39, 1-38.

7.
Formann, A. K. (2003). Latent class model diagnosis from a frequentist point of view, Biometrics, 59, 189-196. crossref(new window)

8.
Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models, Biometrika, 61, 215-231. crossref(new window)

9.
Hingson, R., Heeren, T., Jamanka, A. and Howard, J. (2000). Age of drinking onset and unintentional injury involvement after drinking, Journal of the American Medical Association, 284, 1527-1533. crossref(new window)

10.
Hingson, R., Heeren, T. and Zakocs, R. (2001). Age of drinking onset and involvement in physical fights after drinking, Pediatrics, 108, 872-877. crossref(new window)

11.
Langeheine, R., Pannekoek, J. and Van de Pol, F. (1996). Bootstrapping goodness-of-fit measures in categorical data analysis, Sociological Methods and Research, 24, 492-516. crossref(new window)

12.
Lynskey, M. T., Bucholz, K. K., Madden, P. A. F. and Health, A. C. (2007). Early-onset alcohol-use behaviors and subsequent alcohol-related driving risks in young women: A twin study, Journal of Studies on Alcohol and Drugs, 68, 798-804.

13.
McHugh, R. B. (1956). Efficient estimation and local identification in latent class analysis, Psychometrika, 21, 331-347. crossref(new window)

14.
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys, Wiley, New York.