IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v172y2009i3p599-613.html
   My bibliography  Save this article

Multilevel multivariate modelling of childhood growth, numbers of growth measurements and adult characteristics

Author

Listed:
  • Harvey Goldstein
  • Daphne Kounali

Abstract

Summary. A general latent normal model for multilevel data with mixtures of response types is extended in the case of ordered responses to deal with variates having a large number of categories and including count data. An example is analysed by using repeated measures data on child growth and adult measures of body mass index and glucose. Applications are described that are concerned with the flexible prediction of adult measurements from collections of growth measurements and for studying the relationship between the number of measurement occasions and growth trajectories.

Suggested Citation

  • Harvey Goldstein & Daphne Kounali, 2009. "Multilevel multivariate modelling of childhood growth, numbers of growth measurements and adult characteristics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 599-613, June.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:3:p:599-613
    DOI: 10.1111/j.1467-985X.2008.00576.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-985X.2008.00576.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-985X.2008.00576.x?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
    ---><---

    Citations

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


    Cited by:

    1. Steele, Fiona & Clarke, Paul & Leckie, George & Allan, Julia & Johnston, Derek, 2017. "Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes: an application to the effects of stress on the cognitive function of nurses," LSE Research Online Documents on Economics 64893, London School of Economics and Political Science, LSE Library.
    2. Fiona Steele & Paul Clarke & George Leckie & Julia Allan & Derek Johnston, 2017. "Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes: an application to the effects of stress on the cognitive function of nurses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 263-283, January.
    3. Shin Yongyun & Raudenbush Stephen W., 2013. "Efficient Analysis of Q-Level Nested Hierarchical General Linear Models Given Ignorable Missing Data," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 109-133, September.
    4. Yuda Zhu & Robert E. Weiss, 2013. "Modeling Seroadaptation and Sexual Behavior Among HIV-super-+ Study Participants with a Simultaneously Multilevel and Multivariate Longitudinal Count Model," Biometrics, The International Biometric Society, vol. 69(1), pages 214-224, March.
    5. Francesco Lagona & Antonello Maruotti & Fabio Padovano, 2015. "Multilevel multivariate modelling of legislative count data, with a hidden Markov chain," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 705-723, June.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssa:v:172:y:2009:i:3:p:599-613. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.