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Advances in Group-Based Trajectory Modeling and an SAS Procedure for Estimating Them

Author

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  • Bobby L. Jones

    (Carnegie Mellon, Pittsburgh, PA)

  • Daniel S. Nagin

    (Carnegie Mellon, Pittsburgh, PA)

Abstract

This article is a follow-up to Jones, Nagin, and Roeder (2001), which described an SAS procedure for estimating group-based trajectory models. Group-based trajectory is a specialized application of finite mixture modeling and is designed to identify clusters of individuals following similar progressions of some behavior or outcome over age or time. This article has two purposes. One is to summarize extensions of the methodology and of the SAS procedure that have been developed since Jones et al. The other is to illustrate how group-based trajectory modeling lends itself to presentation of findings in the form of easily understood graphical and tabular data summaries.

Suggested Citation

  • Bobby L. Jones & Daniel S. Nagin, 2007. "Advances in Group-Based Trajectory Modeling and an SAS Procedure for Estimating Them," Sociological Methods & Research, , vol. 35(4), pages 542-571, May.
    • Handle: RePEc:sae:somere:v:35:y:2007:i:4:p:542-571
    DOI: 10.1177/0049124106292364
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    References listed on IDEAS

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    1. Amelia Haviland & Daniel Nagin, 2005. "Causal inferences with group based trajectory models," Psychometrika, Springer;The Psychometric Society, vol. 70(3), pages 557-578, September.
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    Cited by:

    1. Min Hua Jen & Ron Johnston & Kelvyn Jones & Richard Harris & Axel Gandy, 2010. "International Variations In Life Expectancy: A Spatio‐Temporal Analysis," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 101(1), pages 73-90, February.
    2. Colen, Cynthia G. & Ramey, David M. & Cooksey, Elizabeth C. & Williams, David R., 2018. "Racial disparities in health among nonpoor African Americans and Hispanics: The role of acute and chronic discrimination," Social Science & Medicine, Elsevier, vol. 199(C), pages 167-180.
    3. Kati Kuitto & Janne Salonen & Jan Helmdag, 2019. "Gender Inequalities in Early Career Trajectories and Parental Leaves: Evidence from a Nordic Welfare State," Social Sciences, MDPI, vol. 8(9), pages 1-16, September.
    4. Sridharan, Sanjeev & Jones, Bobby & Caudill, Barry & Nakaima, April, 2016. "Steps towards incorporating heterogeneities into program theory: A case study of a data-driven approach," Evaluation and Program Planning, Elsevier, vol. 58(C), pages 88-97.
    5. LEBIHAN, Laetitia & MAO TAKONGMO, Charles Olivier, 2018. "Mathematics Trajectories and Risk Factors During Childhood," MPRA Paper 88612, University Library of Munich, Germany.
    6. Ballestar, María Teresa & Doncel, Luis Miguel & Sainz, Jorge & Ortigosa-Blanch, Arturo, 2019. "A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    7. Omar N. Solinger & Woody van Olffen & Robert A. Roe & Joeri Hofmans, 2013. "On Becoming (Un)Committed: A Taxonomy and Test of Newcomer Onboarding Scenarios," Organization Science, INFORMS, vol. 24(6), pages 1640-1661, December.
    8. Dirlam, Jonathan & Zheng, Hui, 2017. "Job satisfaction developmental trajectories and health: A life course perspective," Social Science & Medicine, Elsevier, vol. 178(C), pages 95-103.
    9. Corrado, Raymond R. & McCuish, Evan C. & Hart, Stephen D. & DeLisi, Matt, 2015. "The role of psychopathic traits and developmental risk factors on offending trajectories from early adolescence to adulthood: A prospective study of incarcerated youth," Journal of Criminal Justice, Elsevier, vol. 43(4), pages 357-368.
    10. McCuish, Evan C. & Corrado, Raymond R. & Hart, Stephen D. & DeLisi, Matt, 2015. "The role of symptoms of psychopathy in persistent violence over the criminal career into full adulthood," Journal of Criminal Justice, Elsevier, vol. 43(4), pages 345-356.

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