Bayesian Two-Level Model for Repeated Partially Ordered Responses: Application to Adolescent Smoking Behavior Analysis

Autor/inn/en	Wang, Xiaoqing; Wu, Haotian; Feng, Xiangnan; Song, Xinyuan
Titel	Bayesian Two-Level Model for Repeated Partially Ordered Responses: Application to Adolescent Smoking Behavior Analysis
Quelle	In: Sociological Methods & Research, 50 (2021) 4, S.1515-1551 (37 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Song, Xinyuan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124119826149
Schlagwörter	Bayesian Statistics; Health Behavior; Smoking; Case Studies; Longitudinal Studies; Markov Processes; Monte Carlo Methods; Adolescents; Statistical Inference; Risk; National Surveys; Classification; National Longitudinal Survey of Youth + Suchen Sie Ihr Suchwort? Health behaviour; Gesundheitsverhalten; Rauchen; Case study; Fallstudie; Case Study; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Markowscher Prozess; Monte-Carlo-Methode; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Inferential statistics; Schließende Statistik; Risiko; Classification system; Klassifikation; Klassifikationssystem
Abstract	Given the questionnaire design and the nature of the problem, partially ordered data that are neither completely ordered nor completely unordered are frequently encountered in social, behavioral, and medical studies. However, early developments in partially ordered data analysis are very limited and restricted only to cross-sectional data. In this study, we propose a Bayesian two-level regression model for analyzing repeated partially ordered responses in longitudinal data. The first-level model is defined for partially ordered observations of interest that are taken at each time point nested within individuals, while the second-level model is defined for individuals to assess the effects of their characteristics on the first-level model. A full Bayesian approach with the Markov chain Monte Carlo algorithm is developed for statistical inference. Simulation studies demonstrate the satisfactory performance of the developed methodology. The methodology is then applied to a longitudinal study on adolescent smoking behavior. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01