Investigating Stage-Sequential Growth Mixture Models with Multiphase Longitudinal Data

Su Young Kim, Jee Seon Kim

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)293-319
Number of pages27
JournalStructural Equation Modeling
Volume19
Issue number2
DOIs
StatePublished - Apr 2012

Keywords

  • growth mixture modeling
  • latent growth modeling
  • longitudinal data analysis
  • multiphase longitudinal data
  • stage-sequential models

Fingerprint

Dive into the research topics of 'Investigating Stage-Sequential Growth Mixture Models with Multiphase Longitudinal Data'. Together they form a unique fingerprint.

Cite this