New Approaches to Studying Problem Behaviors: A Comparison of Methods for Modeling Longitudinal, Categorical Adolescent Drinking Data

Autor/inn/en	Feldman, Betsy J.; Masyn, Katherine E.; Conger, Rand D.
Titel	New Approaches to Studying Problem Behaviors: A Comparison of Methods for Modeling Longitudinal, Categorical Adolescent Drinking Data
Quelle	In: Developmental Psychology, 45 (2009) 3, S.652-676 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0012-1649
DOI	10.1037/a0014851
Schlagwörter	Structural Equation Models; Behavior Problems; Student Behavior; Adolescents; Drinking; Alcohol Abuse; Longitudinal Studies; Data Analysis; Research Methodology; Questionnaires; Rural Areas; Computation; High School Students; Junior High School Students + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Trinken; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Auswertung; Research method; Forschungsmethode; Fragebogen; Rural area; Ländlicher Raum; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Junior High Schools; Sekundarstufe I
Abstract	Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model, a growth mixture model, and a latent class growth analysis. They also describe a longitudinal latent class analysis, which requires fewer assumptions than the first 3. Finally, they illustrate all of the models using actual longitudinal adolescent alcohol-use data. They guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention. (Contains 11 figures, 6 tables, and 14 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10