Cited by 1,143Psychometric Methodologies and TestingStatistical Methods and Bayesian InferenceMental Health Research Topics

Methods and measures: growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups

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Ram, Nilam, Grimm, Kevin J.·2009·International Journal of Behavioral Development

Abstract

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

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Crossref · 10.1177/0165025409343765

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  • Growth mixture modeling is a statistical method designed to identify classes of individuals who are homogeneous in terms of their longitudinal change in a set of variables of interest.

    “Growth mixture modeling (GMM) is a statistical method designed to identify classes of 90 individuals who are homogeneous in terms of their longitudinal change in a set of 91 variables of interest (Muthén, 2006; Ram and Grimm, 2009).”

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  1. Bauer DJ Curran PJ 2003a Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes Psychological Methods 8 338 363 14596495
  2. Bauer DJ Curran PJ 2003b Overextraction of latent trajectory classes: Much ado about nothing? Reply to Risdskopf (2003), Muthen (2003), and Cudeck and Henly (2003) Psychological Methods 8 384 393 14596495
  3. Bauer DJ Curran PJ 2004 The integration of continuous and discrete latent variable models: Potential problems and promising opportunities Psychological Methods 9 3 29 15053717
  4. Bollen KA Curran PJ 2006 Latent curve models: A structural equation perspective New York Wiley
  5. Bryk AS Raudenbush SW 1987 Application of hierarchical linear models to assessing change Psychological Bulletin 101 147 158
  6. Bryk AS Raudenbush SW 1992 Hierarchical Linear Models: Applications and Data Analysis Newbury Park CA Sage Publications
  7. Greenbaum PE Del Boca FK Darkes J Wang C Goldman MS 2005 Variation in the drinking trajectories of freshmen college students Journal of Consulting and Clinical Psychology 73 229 238 15796630
  8. Grimm KJ Ram N (in press) Non-linear growth models in Mplus and SAS Structural Equation Modeling: A Multidisciplinary Journal PMC3717396 23882134
  9. Grimm KJ Ram N (in press) A second order growth mixture model for developmental research Research in Human Development
  10. Harring J 2005 Nonlinear mixed effects mixture model: A model for clustering nonlinear longitudinal profile Unpublished doctoral dissertation University of Minnesota
  11. Heim C Elhert U Hellhammer D 2000 The potential role of hypocortisolism in the pathophysiology of stress-related bodily disorders Psychoneuroendricinology 25 1 35 10633533
  12. Hipp JR Bauer DJ 2006 Local solutions in the estimation of growth mixture models Psychological Methods 11 36 53 16594766
  13. Kelley K 2005 Estimating nonlinear change models in heterogeneous populations when class membership is unknown: Defining and developing the latent classification differential change model Unpublished Doctoral Dissertation University of Notre Dame
  14. Little RJA Rubin DB 1987 Statistical Analysis with Missing Data J. Wiley & Sons New York
  15. Masyn K (in press) Discrete-time survival factor mixture analysis for low-frequency recurrent event histories Research in Human Development 4 ???-??? PMC3905990 24489519
  16. McArdle JJ Epstein DB 1987 Latent growth curves within developmental within developmental structural equation models Child Development 58 110 133 3816341
  17. McArdle JJ Nesselroade JR Shinka J Velicer W 2003 Growth curve analysis in contemporary psychological research Comprehensive handbook of psychology, Volume two: Research methods in psychology 447 480 New York Wiley
  18. McLachlan GJ Peel D 2000 Finite Mixture Models New York John Wiley & Sons
  19. Muthén B 2003 Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003) Psychological Methods 8 369 377 14596497
  20. Muthén B Kaplan D 2004 Latent variable analysis. Growth mixture modeling and related techniques for longitudinal data The Sage handbook of quantitative methodology for the social sciences 345 368 Thousand Oaks Sage Publications
  21. Muthén BO Shedden K 1999 Finite mixture modeling with mixture outcomes using the EM algorithm Biometrics 55 463 469 11318201
  22. Muthén BO Masyn K 2005 Discrete-time survival mixture analysis Journal of Educational and Behavioral Statistics 30 27 58
  23. Muthén BO Muthén LK 2000 Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes Alcoholism: Clinical and Experimental Research 24 882 891 10888079
  24. Preacher KJ Wichman AL MacCallum RC Briggs NE 2008 Latent growth curve modeling Thousand Oaks, CA Sage Publications
  25. Ram N Grimm K 2007 Using simple and complex growth models to articulate developmental change: Matching theory to method International Journal of Behavioral Development 31 303 316
  26. Rindskopf D 2003 Mixture or homogeneous? Comment on Bauer and Curran (2003) Psychological Methods 8 364 368 14596496
  27. Seeman TE Berkman LF Gulanski B Robbins R Greenspan S Charpentier P Rowe J 1995 Self-esteem and neuroendocrine response to challenge: MacArthur Successful Aging Studies Psychosomatic Research 39 69 84 7760305
  28. Seeman TE Singer B Charpentier P 1995 Gender differences in pattern of HPA axis response to challenge: MacArthur Studies of Successful Aging Psychoneuroendicrinology 20 711 725 8848517
  29. Singer JD Willet JB 2003 Applied longitudinal data analysis: Modeling change and event occurrence NY Oxford University Press
  30. Wohlwill JF 1973 The study of behavioral development San Diego, CA Academic Press

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Authors
Ram, N., & Grimm, K. J.
Affiliations
  • The Pennsylvania State University, USA, and Max Planck Institute for Human Development, Germany,
  • Max Planck Institute for Human Development (DE)
Venue
International Journal of Behavioral Development
Published
2009-10-26
Volume
33
Issue
6
Pages
565-576
ISSN
  • 0165-0254 (ISSN-L)
  • 1464-0651
OpenAlex
W2121236536
Cited by
1,143
References
6
Citations by year
2026: 126 · 2025: 116 · 2024: 109 · 2023: 111 · 2022: 105 · 2021: 106 · 2020: 89 · 2019: 80 · 2018: 87 · 2017: 59 · 2016: 54 · 2015: 35 · 2014: 17 · 2013: 19 · 2012: 14
FWCI
13.55
Type
journal article
Language
en
Open access
Locations
  • International Journal of Behavioral Development (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • Max Planck Institute for Plasma Physics (submittedVersion)
Funders
National Institute on Aging (T32AG020500)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
growth mixture modeling, longitudinal change, cortisol stress response

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