Group-Based Trajectory Modeling: An Overview
Abstract
This article provides an overview of a group-based statistical methodology for analyzing developmental trajectories - the evolution of an outcome over age or time. Across all application domains, this group-based statistical method lends itself to the presentation of findings in the form of easily understood graphical and tabular data summaries. In so doing, the method provides statistical researchers with a tool for figuratively painting a statistical portrait of the predictors and consequences of distinct trajectories of development. Data summaries of this form have the great advantage of being accessible to nontechnical audiences and quickly comprehensible to audiences that are technically sophisticated. Examples of the application of the method are provided. A detailed account of the statistical underpinnings of the method and a full range of applications are provided by the author in a previous study.
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The trajectories are representative of latent classes that describe patterns of individual behaviors over time.
“the trajectories are representative of latent classes that describe patterns of individual behaviors over time.”
From An Officer-Level Examination of the Prevalence and Correlates of Police Body-Worn Camera Activation (2025) · cites this paper as Nagin, 2014
Individual observations are allowed to retain distinct trends in the outcome variable over time, even when categorized into the same trajectory.
“Individual observations are allowed to retain distinct trends in the outcome variable over time, even when categorized into the same trajectory (Nagin, 2014).”
From An Officer-Level Examination of the Prevalence and Correlates of Police Body-Worn Camera Activation (2025) · cites this paper as Nagin, 2014
Cites
Group-Based Modeling of Development
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“2 of Nagin (2005) , for count data P.Yi/ is specified as the zero-inflated Poisson distribution, for censored data it is specified as the censored normal distribution, and for binary data, it is specified as the binary logit distribution.”
BackgroundMethodInfluentialSelf-citationThe SAGE Handbook of Quantitative Methodology for the Social Sciences
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Looking beyond the mean in grief trajectories: A prospective, population-based cohort study
Nielsen, M. K., Carlsen, A. H., Neergaard, M. A., Bidstrup, P. E., & Guldin, M.-B. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2018.10.007 · Cited by 138 · see citing works
“PG sum scores were modelled using the censored normal distribution (Nagin, 2014, Nagin and Odgers, 2010).”
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An Officer-Level Examination of the Prevalence and Correlates of Police Body-Worn Camera Activation
Huff, J., White, M. D., Malm, A. E., & Katz, C. M. (2025) · Police Quarterly · doi:10.1177/10986111241241751 · Cited by 7 · see citing works
- Authors
- Nagin, D. S.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Nagin, Daniel S.
- ORCID
- Nagin, Daniel S.
- Venue
- Annals of Nutrition and Metabolism
- Publisher
- S. Karger AG
- Published
- 2014
- Volume
- 65
- Issue
- 2-3
- Pages
- 205-210
- ISSN
- 0250-6807 (ISSN-L)
- 1421-9697
- OpenAlex
- W2095475275
- Cited by
- 700
- References
- 3
- Citations by year
- 2026: 91 · 2025: 126 · 2024: 108 · 2023: 96 · 2022: 88 · 2021: 56 · 2020: 40 · 2019: 22 · 2018: 25 · 2017: 11 · 2016: 14 · 2015: 8 · 2014: 5 · 2013: 4 · 2012: 1
- FWCI
- 13.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Nutrition and Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0647576)
- National Institute of Mental Health (RO1 MH65611-01A2, R01MH65611-01A2, R01MH065611)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Agriculture, Soil, Plant Science
- Keywords
- group-based trajectory modeling, developmental trajectories, longitudinal data, age-related changes, trajectory analysis
- MeSH terms
- Adolescent, Aggression, Child, Child Development, Humans, Longitudinal Studies, Male, Models, Theoretical, Likelihood Functions
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