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Group-Based Modeling of Development
Abstract
This book provides a systematic exposition of a group-based statistical method for analyzing longitudinal data in the social and behavioral sciences and in medicine. The methods can be applied to a wide range of data, such as that describing the progression of delinquency and criminality over the life course, or changes in income over time.
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“The mean a posteriori probability of being assigned to high- and low-risk groups was above the recommended .7 threshold (females: high risk M = .86, low risk M = .85; males: high risk M = .80, low risk M = .91), suggesting that hard classification into high and low groups can be confidently used as an exposure variable (Nagin, 2005).”
From Pathways of association between disordered eating in adolescence and mental health outcomes in young adulthood during the COVID-19 pandemic (2023) · cites this paper as Nagin, 2005
In the citing paper
“The odds of correct classification were also examined for each model specification to guide final model selection (Nagin, 2005;Wheeler, 2016).”
From An Officer-Level Examination of the Prevalence and Correlates of Police Body-Worn Camera Activation (2025) · cites this paper as Nagin, 2005
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“The first approach involves the class of analyses that can create subgroups of individuals based off their responses, such as mixture models (latent class analysis, latent profile analysis; McCutcheon, 1987; Nagin, 2005).”
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Metadata
- Authors
- NAGIN, D.
- Corresponding author
- NAGIN, Daniel
- ORCID
- NAGIN, Daniel
- Venue
- Harvard University Press eBooks
- Publisher
- Harvard University Press
- Published
- 2005-01-01
- OpenAlex
- W1598612182
- Cited by
- 4,164
- References
- 0
- Citations by year
- 2026: 234 · 2025: 288 · 2024: 271 · 2023: 264 · 2022: 297 · 2021: 323 · 2020: 263 · 2019: 270 · 2018: 225 · 2017: 220 · 2016: 222 · 2015: 241 · 2014: 155 · 2013: 154 · 2012: 176
- FWCI
- 19.40
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Harvard University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- longitudinal analysis, life course, delinquency, criminality, income change, developmental trajectories
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