Using Group-Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories

Sheila Frankfurt, Patricia Frazier, Moin Syed, Kyoung Rae Jung

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Many issues of interest to counseling psychologists involve questions regarding how individuals change over time. Although counseling psychologists often examine average levels of change, statistical methods can also identify patterns of change over time by empirically grouping together individuals with similar patterns of change (e.g., group-based trajectory modeling and latent growth mixture modeling). The purpose of this article is to provide an overview of these methods for counseling psychologists. We discuss the conceptual frameworks and assumptions of average-level and person-centered techniques such as group-based trajectory modeling and latent growth mixture modeling. We provide a nontechnical guide for conducting these analyses using data from a study of psychotherapy outcomes in a sample of mental health center clients (N = 1,050). We discuss caveats associated with these methods, including the potential for overinterpreting nongeneralizable results. Last, we suggest best practices for reporting and interpreting results.

Original languageEnglish (US)
Pages (from-to)622-660
Number of pages39
JournalCounseling Psychologist
Volume44
Issue number5
DOIs
StatePublished - Jul 1 2016

Keywords

  • group-based trajectory modeling
  • latent class growth analysis
  • latent growth mixture modeling
  • psychotherapy outcomes
  • quantitative

Fingerprint Dive into the research topics of 'Using Group-Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories'. Together they form a unique fingerprint.

Cite this