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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
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“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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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