Poisson growth mixture modeling of intensive longitudinal data: An application to smoking cessation behavior Journal Article


Authors: Shiyko, M. P.; Li, Y.; Rindskopf, D.
Article Title: Poisson growth mixture modeling of intensive longitudinal data: An application to smoking cessation behavior
Abstract: Intensive longitudinal data (ILD) have become increasingly common in the social and behavioral sciences; count variables, such as the number of daily smoked cigarettes, are frequently used outcomes in many ILD studies. We demonstrate a generalized extension of growth mixture modeling (GMM) to Poisson-distributed ILD for identifying qualitatively distinct trajectories in the context of developmental heterogeneity in count data. Accounting for the Poisson outcome distribution is essential for correct model identification and estimation. In addition, setting up the model in a way that is conducive to ILD measures helps with data complexities-large data volume, missing observations, and differences in sampling frequency across individuals. We present technical details of model fitting, summarize an empirical example of patterns of smoking behavior change, and describe research questions the generalized GMM helps to address. © Taylor & Francis Group, LLC.
Keywords: smoking cessation; intensive longitudinal data; count data; generalized growth mixture modeling; model enumeration
Journal Title: Structural Equation Modeling
Volume: 19
Issue: 1
ISSN: 1070-5511
Publisher: Taylor & Francis Group  
Date Published: 2012-01-01
Start Page: 65
End Page: 85
Language: English
DOI: 10.1080/10705511.2012.634722
PROVIDER: scopus
PMCID: PMC3294500
PUBMED: 22408365
DOI/URL:
Notes: --- - "Export Date: 1 October 2012" - "Source: Scopus"
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  1. Yuelin Li
    138 Li