IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v159y1996i2p249-263.html
   My bibliography  Save this article

Logistic Regression Models for Binary Panel Data with Attrition

Author

Listed:
  • Garrett M. Fitzmaurice
  • Anthony F. Heath
  • Peter Clifford

Abstract

We discuss ways of analysing panel data when the response is binary and there is attrition or drop‐out. In general, informative or non‐ignorable drop‐out models are non‐identifiable and arbitrary constraints on the drop‐out model must be imposed before carrying out a statistical analysis. The problem is particularly acute when predictors as well as response variables are lost by attrition. We describe a likelihood‐based method for dealing with the drop‐out process in this difficult case and show how the effect of non‐identifiability can be reduced by importing additional data from a cross‐sectional survey of the same population. The methods are primarily motivated by data from the 1987–92 British Election Panel Study and the 1992 British Election Study.

Suggested Citation

  • Garrett M. Fitzmaurice & Anthony F. Heath & Peter Clifford, 1996. "Logistic Regression Models for Binary Panel Data with Attrition," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 249-263, March.
    • Handle: RePEc:bla:jorssa:v:159:y:1996:i:2:p:249-263
    DOI: 10.2307/2983172
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2983172
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2983172?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. Janet Tsin-Yee Leung, 2021. "Overparenting, Parent-Child Conflict and Anxiety among Chinese Adolescents: A Cross-Lagged Panel Study," IJERPH, MDPI, vol. 18(22), pages 1-14, November.
    2. Andrew M. Jones & Xander Koolman & Nigel Rice, 2006. "Health‐related non‐response in the British Household Panel Survey and European Community Household Panel: using inverse‐probability‐weighted estimators in non‐linear models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 543-569, July.
    3. Jolene Birmingham & Garrett M. Fitzmaurice, 2002. "A Pattern-Mixture Model for Longitudinal Binary Responses with Nonignorable Nonresponse," Biometrics, The International Biometric Society, vol. 58(4), pages 989-996, December.

    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:159:y:1996:i:2:p:249-263. 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.