IDEAS home Printed from https://ideas.repec.org/a/spr/alstar/v95y2011i4p415-434.html
   My bibliography  Save this article

Growth mixture models in longitudinal research

Author

Listed:
  • Jost Reinecke
  • Daniel Seddig

Abstract

No abstract is available for this item.

Suggested Citation

  • Jost Reinecke & Daniel Seddig, 2011. "Growth mixture models in longitudinal research," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 415-434, December.
  • Handle: RePEc:spr:alstar:v:95:y:2011:i:4:p:415-434
    DOI: 10.1007/s10182-011-0171-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10182-011-0171-4
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10182-011-0171-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Neal O. Jeffries, 2003. "A note on 'Testing the number of components in a normal mixture'," Biometrika, Biometrika Trust, vol. 90(4), pages 991-994, December.
    2. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    3. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
    4. Joseph Hilbe, 1994. "Negative binomial regression," Stata Technical Bulletin, StataCorp LP, vol. 3(18).
    5. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    6. Ledyard Tucker, 1958. "Determination of parameters of a functional relation by factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(1), pages 19-23, March.
    7. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Harry Haupt & Cheng Hsiao, 2011. "Introduction to the special issue: interdisciplinary aspects of panel data analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 325-327, December.
    2. Eldad Davidov & Stefan Thörner & Peter Schmidt & Stefanie Gosen & Carina Wolf, 2011. "Level and change of group-focused enmity in Germany: unconditional and conditional latent growth curve models with four panel waves," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 481-500, December.
    3. Wei Zhao & Limin Peng & John Hanfelt, 2022. "Semiparametric latent class analysis of recurrent event data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1175-1197, September.
    4. Kristian Kleinke & Mark Stemmler & Jost Reinecke & Friedrich Lösel, 2011. "Efficient ways to impute incomplete panel data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 351-373, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
    2. Kiero Guerra-Peña & Zoilo Emilio García-Batista & Sarah Depaoli & Luis Eduardo Garrido, 2020. "Class enumeration false positive in skew-t family of continuous growth mixture models," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-19, April.
    3. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    4. Eldad Davidov & Stefan Thörner & Peter Schmidt & Stefanie Gosen & Carina Wolf, 2011. "Level and change of group-focused enmity in Germany: unconditional and conditional latent growth curve models with four panel waves," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 481-500, December.
    5. Pietro Lovaglio & Mario Mezzanzanica, 2013. "Classification of longitudinal career paths," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 989-1008, February.
    6. Joanna F. Dipnall & Belinda J. Gabbe & Warwick J. Teague & Ben Beck, 2020. "Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
    7. Guido Alessandri & Michele Vecchione & Brent Donnellan & John Tisak, 2013. "An Application of the LC-LSTM Framework to the Self-esteem Instability Case," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 769-792, October.
    8. Kenneth A. Bollen & Patrick J. Curran, 2004. "Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions," Sociological Methods & Research, , vol. 32(3), pages 336-383, February.
    9. Jumin Park & Debra K. Moser & Kathleen Griffith & Jeffrey R. Harring & Meg Johantgen, 2019. "Exploring Symptom Clusters in People With Heart Failure," Clinical Nursing Research, , vol. 28(2), pages 165-181, February.
    10. Casey Codd & Robert Cudeck, 2014. "Nonlinear Random-Effects Mixture Models for Repeated Measures," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 60-83, January.
    11. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.
    12. Morgan, Grant B. & Hodge, Kari J. & Baggett, Aaron R., 2016. "Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 146-161.
    13. Lu, Zhenqiu (Laura) & Zhang, Zhiyong, 2014. "Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 220-240.
    14. Roberta Adorni & Andrea Greco & Marco D’Addario & Francesco Zanatta & Francesco Fattirolli & Cristina Franzelli & Alessandro Maloberti & Cristina Giannattasio & Patrizia Steca, 2022. "Sense of Coherence Predicts Physical Activity Maintenance and Health-Related Quality of Life: A 3-Year Longitudinal Study on Cardiovascular Patients," IJERPH, MDPI, vol. 19(8), pages 1-14, April.
    15. Anindita Chakravarty & Rajdeep Grewal & V. Sambamurthy, 2013. "Information Technology Competencies, Organizational Agility, and Firm Performance: Enabling and Facilitating Roles," Information Systems Research, INFORMS, vol. 24(4), pages 976-997, December.
    16. Jeffrey R. Harring, 2009. "A Nonlinear Mixed Effects Model for Latent Variables," Journal of Educational and Behavioral Statistics, , vol. 34(3), pages 293-318, September.
    17. Heike Heidemeier & Anja Göritz, 2013. "Individual Differences in How Work and Nonwork Life Domains Contribute to Life Satisfaction: Using Factor Mixture Modeling for Classification," Journal of Happiness Studies, Springer, vol. 14(6), pages 1765-1788, December.
    18. Bartolucci Francesco & Murphy Thomas Brendan, 2015. "A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(4), pages 193-203, December.
    19. Chen, Yushun & Lin, Lian-Shin, 2010. "Structural equation-based latent growth curve modeling of watershed attribute-regulated stream sensitivity to reduced acidic deposition," Ecological Modelling, Elsevier, vol. 221(17), pages 2086-2094.
    20. Wei Zhao & Limin Peng & John Hanfelt, 2022. "Semiparametric latent class analysis of recurrent event data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1175-1197, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:alstar:v:95:y:2011:i:4:p:415-434. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.