Beyond SEM: General latent variable modeling
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Person-centered analyses such as GMA are specifically designed to explain longitudinal heterogeneity by separating a general population into profiles with qualitatively and quantitatively different trajectories.
“Person-centered analyses, such as GMA, are specifically designed to explain longitudinal heterogeneity by separating a general population into profiles of students presenting qualitatively and quantitatively different trajectories (Muthén, 2002).”
From University students' need satisfaction trajectories: A growth mixture analysis (2019) · cites this paper as Muthén, 2002
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Barrett, B., Muller, D., Hayer, S., Ewers, T., Chase, J., Barnet, J. H., & Brown, R. (2019) · EXPLORE · doi:10.1016/j.explore.2018.07.005 · Cited by 19 · see citing works
Raykov, T., Goldammer, P., Marcoulides, G. A., Li, T., & Menold, N. (2018) · Educational and Psychological Measurement · doi:10.1177/0013164417719948 · Cited by 13 · see citing works
Shaw, A. M., Halliday, E. R., Tonarely, N. A., & Ehrenreich-May, J. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.10.048 · Cited by 14 · see citing works
- Authors
- Muthén, B. O.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Muthén, Bengt O.
- ORCID
- Muthén, Bengt O.
- Venue
- Behaviormetrika
- Published
- 2002-01
- Volume
- 29
- Issue
- 1
- Pages
- 81-117
- ISSN
- 0385-7417 (ISSN-L)
- 1349-6964
- OpenAlex
- W2079257116
- Cited by
- 1,074
- References
- 93
- Citations by year
- 2026: 33 · 2025: 56 · 2024: 31 · 2023: 44 · 2022: 48 · 2021: 56 · 2020: 50 · 2019: 56 · 2018: 37 · 2017: 46 · 2016: 52 · 2015: 64 · 2014: 72 · 2013: 53 · 2012: 63
- FWCI
- 29.64
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-02-13)
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Behaviormetrika (publishedVersion)
- Funders
- National Institute on Alcohol Abuse and Alcoholism (K02AA000230, K02 AA 00230-01)
- National Institute on Drug Abuse
- 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 › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- latent variable models, structural equation modeling, random effects, missing data, hierarchical data, mixture models, latent classes, clustering, factor models, growth curve models, multilevel models, latent class models, discrete-time survival models, psychometrics, measurement error, unified framework
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