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Article
Modelling and predicting complex patterns of change using growth component models: An application to depression trajectories in cancer patients
European Journal of Developmental Psychology
  • Christian Geiser, Utah State University
  • Axel Mayer, Friedrich Schiller University
  • Frank J. Infurna, Humboldt University
  • Christiane Fiege, Eberhard Carls University
Document Type
Article
Publisher
Taylor & Francis
Publication Date
10-23-2012
DOI
10.1080/17405629.2012.732721
Abstract
In this paper we present a general and flexible framework for constructively defining growth components to model complex change processes. Building on the concepts of the latent state-trait theory (LST theory; Steyer, Ferring, & Schmitt, 1992), we develop structural equation models containing latent variables that represent latent growth (change) components of interest. We formulate these models based on an approach presented by Mayer, Steyer and Mueller (2012). We discuss an application to the longitudinal course of depression in 2,794 individuals from the Health and Retirement Study, who experienced cancer diagnosis over the course of the study. We found that (1) on average, the depression trajectories showed a steep increase after diagnosis as well as an adaptation phase where levels returned back to levels prior to diagnosis, and (2) individual differences in change were large and could be partly explained by marital status and cognitive functioning.
Citation Information
Christian Geiser, Axel Mayer, Frank J. Infurna and Christiane Fiege. "Modelling and predicting complex patterns of change using growth component models: An application to depression trajectories in cancer patients" European Journal of Developmental Psychology Vol. 10 Iss. 1 (2012) p. 40 - 59
Available at: http://works.bepress.com/christian-geiser/36/