Modeling and Predicting Complex Patterns of Change Using Growth Component Models: An Application to Depression Trajectories in Cancer Patients Article Swipe
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/6kd3w
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 & Mueller (in press). We discuss an application to the longitudinal course of depression in 2794 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.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31234/osf.io/6kd3w
- OA Status
- gold
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4245955215