Objective: Latent growth mixture modeling (LGMM) and latent class growth analysis (LCGA) are methods of identifying subgroups of individuals with similar trajectories during the course of psychotherapy. Due to inconsistent methodology, previous LGMM/LCGA psychotherapy research has led to inconsistent findings. The purpose of this study was to contribute to our understanding of individual differences in change trajectories during psychotherapy using LGMM/LCGA by attempting to replicate a previous study by Owen et al. (2015. Trajectories of change in psychotherapy. Journal of Clinical Psychology, 71(9), 817–827). Method: This study used LGMM/LCGA to model trajectories of change in a sample of 2538 psychotherapy clients at a university student counseling center. This was a secondary analysis of naturalistically-collected outcome data using The Behavioral Health Measure. Results: LGMM models did not converge. A 2-class LCGA model was selected based on fit statistics and parsimony. One class was labeled as Slow and Steady Change Before Plateau, whereas the other was labeled as Early Rapid Change Before Plateau. We also extended these findings by considering variables associated with class membership. Conclusions: These classes followed similar trajectories to two of the classes identified by Owen et al. These results indicate that latent trajectory modeling may lead to replicable findings. Furthermore, these results have implications for managing expectations about change in psychotherapy.
Bibliographical noteFunding Information:
Findings from the current study were presented at the 2019 International Convention of Psychological Science in Paris, France and at the 2019 American Psychological Association Convention in Chicago, IL.
© 2020 Society for Psychotherapy Research.
Copyright 2021 Elsevier B.V., All rights reserved.
- outcome research
- statistical methodology
PubMed: MeSH publication types
- Journal Article