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ERIC Number: EJ970506
Record Type: Journal
Publication Date: 2012
Pages: 27
Abstractor: As Provided
Reference Count: 52
ISSN: ISSN-1070-5511
Investigating Stage-Sequential Growth Mixture Models with Multiphase Longitudinal Data
Kim, Su-Young; Kim, Jee-Seon
Structural Equation Modeling: A Multidisciplinary Journal, v19 n2 p293-319 2012
This article investigates three types of stage-sequential growth mixture models in the structural equation modeling framework for the analysis of multiple-phase longitudinal data. These models can be important tools for situations in which a single-phase growth mixture model produces distorted results and can allow researchers to better understand population heterogeneity and growth over multiple phases. Through theoretical and empirical comparisons of the models, the authors discuss strategies with respect to model selection and interpreting outcomes. The unique attributes of each approach are illustrated using ecological momentary assessment data from a tobacco cessation study. Transitional discrepancy between phases as well as growth factors are examined to see whether they can give us useful information related to a distal outcome, abstinence at 6 months postquit. It is argued that these statistical models are powerful and flexible tools for the analysis of complex and detailed longitudinal data. (Contains 5 tables, 6 figures, and 15 footnotes.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A