The potential of growth mixture modelling
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Growth mixture modeling is a statistical method designed to identify classes of individuals who are homogeneous in terms of their longitudinal change in a set of variables of interest.
“Growth mixture modeling (GMM) is a statistical method designed to identify classes of 90 individuals who are homogeneous in terms of their longitudinal change in a set of 91 variables of interest (Muthén, 2006; Ram and Grimm, 2009).”
From Time in treatment: Examining mental illness trajectories across inpatient psychiatric treatment (2020) · cites this paper as Muthén, 2006
In “Not GMM Versus LCGA, but Both in a General Latent Variable Framework” (page not provided), Muthén describes GMM as modelling trajectory classes. But the paper explicitly says GMM allows within-class variation, so describing individuals in a class as homogeneous in their longitudinal change overstates what the method assumes. (LLM, conf=0.86)
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Cites
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510701575396 · Cited by 11,415 · see citing works
Self-citationGroup-Based Modeling of Development
NAGIN, D. (2005) · doi:10.4159/9780674041318 · Cited by 4,164 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Brown, E. C., Catalano, C. B., Fleming, C. B., Haggerty, K. P., & Abbot, R. D. (2004). Adolescent substance use outcomes in the Raising Healthy Children Project: A two-part latent growth curve analysis. Journal of Consulting and Clinical Psychology, in press.
- Muthe ´n, B. (2004). Latent variable analysis: Growth mixture modelling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.
- Nagin, D. S., & Tremblay, R. (2005). Developmental trajectory groups: Fact or a useful statistical fiction? Criminology, 43(4), 873-903.
- Olsen, M. K, & Schafer, J. L. (2001). A two-part random effects model for semicontinuous longitudinal data. Journal of the American Statistical Association, 96, 730-745.
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The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies
van de Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S., & Vermunt, J. K. (2017) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2016.1247646 · Cited by 721 · see citing works
Depression trajectories during the first year after traumatic brain injury
Bombardier, C. H., Hoekstra, T., Dikmen, S., & Fann, J. R. (2016) · Journal of Neurotrauma · doi:10.1089/neu.2015.4349 · Cited by 95 · see citing works
Growth mixture modeling of depression symptoms following traumatic brain injury
Gomez, R., Skilbeck, C., Thomas, M., & Slatyer, M. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01320 · Cited by 21 · see citing works
Time in treatment: Examining mental illness trajectories across inpatient psychiatric treatment
Oh, H., Lee, J., Kim, S., Rufino, K. A., Fonagy, P., Oldham, J. M., Schanzer, B., & Patriquin, M. A. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.07.001 · Cited by 15 · see citing works
- Authors
- Muthén, B.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Muthén, Bengt
- ORCID
- Muthén, Bengt
- Venue
- Infant and Child Development
- Publisher
- Wiley
- Published
- 2006-11
- Volume
- 15
- Issue
- 6
- Pages
- 623-625
- ISSN
- 1522-7219 (ISSN-L)
- 1522-7227
- OpenAlex
- W2038460442
- Cited by
- 120
- References
- 2
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 2 · 2022: 8 · 2021: 17 · 2020: 6 · 2019: 8 · 2018: 4 · 2017: 10 · 2016: 11 · 2015: 7 · 2014: 9 · 2013: 9 · 2012: 8
- FWCI
- 3.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Infant and Child Development (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- growth mixture modeling, developmental trajectories, longitudinal analysis
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