The DOI matches its Crossref record; 39 of the 40 references it cites were found in a registry or an index.
Analyzing developmental trajectories: A semiparametric, group-based approach.
Abstract
A developmental trajectory describes the course of a behavior over age or time. A group-based method for identifying distinctive groups of individual trajectories within the population and for profiling the characteristics of group members is demonstrated. Such clusters might include groups of "increasers. " "decreasers," and "no changers. " Suitably defined probability distributions are used to handle 3 data types—count, binary, and psychometric scale data. Four capabilities are demonstrated: (a) the capability to identify rather than assume distinctive groups of trajectories, (b) the capability to estimate the proportion of the population following each such trajectory group, (c) the capability to relate group membership probability to individual characteristics and circumstances, and (d) the capability to use the group membership probabilities for various other purposes such as creating profiles of group members. Over the past decade, major advances have been made in methodology for analyzing individual-level developmental trajectories. The two main branches of methodology are hierarchical modeling (Bryk &
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“Unfortunately, despite the pioneering nature of their study, Ratelle and Duchesne (2014) relied on a restricted form of GMA in which it was assumed that all students would correspond exactly to the average longitudinal trajectory identified in their own profile (i.e., latent class growth analysis : Nagin, 1999).”
From University students' need satisfaction trajectories: A growth mixture analysis (2019) · cites this paper as Nagin, 1999
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Cites
Limited-dependent and qualitative variables in econometrics
Maddala, G. S. (1983) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810176 · Cited by 13,240
A Handbook of Child Psychology
(1932) · Nature · doi:10.1038/130331a0 · Cited by 10,101
Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy
Moffitt, T. E. (1993) · Psychological Review · doi:10.1037/0033-295x.100.4.674 · Cited by 9,987
Estimation of Relationships for Limited Dependent Variables
Tobin, J. (1958) · Econometrica · doi:10.2307/1907382 · Cited by 6,996
Bayesian model selection in social research
Raftery, A. E. (1995) · Sociological Methodology · doi:10.2307/271063 · Cited by 6,823
(2006) · Technometrics · doi:10.1198/tech.2006.s417 · Cited by 4,950
Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
Lambert, D. (1992) · Technometrics · doi:10.2307/1269547 · Cited by 3,988
Statistical Analysis of Finite Mixture Distributions.
Lindsay, B. G., Titterington, D. M., Smith, A. F. M., & Makov, U. E. (1987) · Journal of the American Statistical Association · doi:10.2307/2289504 · Cited by 2,905
Heckman, J., & Singer, B. (1984) · Econometrica · doi:10.2307/1911491 · Cited by 2,788
A developmental perspective on antisocial behavior
Patterson, G. R., DeBaryshe, B. D., & Ramsey, E. (1989) · American Psychologist · doi:10.1037/0003-066x.44.2.329 · Cited by 2,048
Cited by
Levesque, R. J. R. (2017) · Encyclopedia of Adolescence · doi:10.1007/978-3-319-32132-5_447-2 · Cited by 4,021
Muthén, B., & Muthén, L. K. (2000) · Alcoholism: Clinical and Experimental Research · doi:10.1111/j.1530-0277.2000.tb02070.x · Cited by 3,149
“The article by Hill, White, Chung, and Hawkins in this issue provides an example of LCGA in the area of binge drinking, and Nagin (1999) gives an overview with applications.”
BackgroundMethodInfluentialGroup-Based Trajectory Modeling in Clinical Research
Nagin, D. S., & Odgers, C. L. (2010) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.121208.131413 · Cited by 2,708
Self-citationA SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories
JONES, B. L., NAGIN, D. S., & ROEDER, K. (2001) · Sociological Methods & Research · doi:10.1177/0049124101029003005 · Cited by 2,370
“All Rights Reserved.”
Self-citationPsychological resilience, positive emotions, and successful adaptation to stress in later life.
Ong, A. D., Bergeman, C. S., Bisconti, T. L., & Wallace, K. A. (2006) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.91.4.730 · Cited by 1,547
“Person-centered approaches such as latent class analysis and growth mixture modeling, in comparison, allow for the consideration of whether hidden categorical variables (classes) explain the trajectories of individuals over time (Nagin, 1999).”
Metadata
- Authors
- Nagin, D. S.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Nagin, Daniel S.
- ORCID
- Nagin, Daniel S.
- Venue
- Psychological Methods
- Published
- 1999-06
- Volume
- 4
- Issue
- 2
- Pages
- 139-157
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2072221629
- Cited by
- 2,346
- References
- 40
- Citations by year
- 2026: 67 · 2025: 62 · 2024: 83 · 2023: 85 · 2022: 89 · 2021: 119 · 2020: 103 · 2019: 115 · 2018: 94 · 2017: 127 · 2016: 121 · 2015: 135 · 2014: 112 · 2013: 106 · 2012: 129
- FWCI
- 21.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Methods (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- developmental trajectories, group-based trajectory modeling, semiparametric models, count data, binary data
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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