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 language | English (US) |
|---|---|
| Pages (from-to) | 622-660 |
| Number of pages | 39 |
| Journal | Counseling Psychologist |
| Volume | 44 |
| Issue number | 5 |
| DOIs | |
| State | Published - Jul 1 2016 |
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Keywords
- group-based trajectory modeling
- latent class growth analysis
- latent growth mixture modeling
- psychotherapy outcomes
- quantitative
Cite this
Using Group-Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories. / Frankfurt, Sheila; Frazier, Patricia A; Syed, Moin; Jung, Kyoung Rae.
In: Counseling Psychologist, Vol. 44, No. 5, 01.07.2016, p. 622-660.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Using Group-Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories
AU - Frankfurt, Sheila
AU - Frazier, Patricia A
AU - Syed, Moin
AU - Jung, Kyoung Rae
PY - 2016/7/1
Y1 - 2016/7/1
N2 - 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.
AB - 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.
KW - group-based trajectory modeling
KW - latent class growth analysis
KW - latent growth mixture modeling
KW - psychotherapy outcomes
KW - quantitative
UR - http://www.scopus.com/inward/record.url?scp=84982980739&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982980739&partnerID=8YFLogxK
U2 - 10.1177/0011000016658097
DO - 10.1177/0011000016658097
M3 - Article
AN - SCOPUS:84982980739
VL - 44
SP - 622
EP - 660
JO - Counseling Psychologist
JF - Counseling Psychologist
SN - 0011-0000
IS - 5
ER -