IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v10y1981i1p3-49.html
   My bibliography  Save this article

Estimating Nonhierarchical and Nested Log-Linear Models

Author

Listed:
  • Jay Magidson
  • James H. Swan
  • Richard A. Berk

Abstract

The recent literature on-log-linear models incorrectly implies that the Iterative Proportional Fitting (IPF) algorithm and associated computer programs such as ECTA can only be used to estimate hierarchical (not nonhierarchical) log-linear models. While ECTA and similar programs are designed for the estimation of hierarchical models, it is shown here that the IPF algorithm (and existing computer programs such as ECTA) can be used to estimate any nonhierarchical model and also many nested log-linear models. The former result follows directly from the symmetry between qualitative/categorical indicator variables and appropriately defined “interaction variables.†The general approach for dichotomous variables is illustrated here using data from the study of “The American Soldier†by Stouffer et al. We also illustrate how the ECTA program can be used to estimate nested models, and show the equivalence between a particular class of nested models and the model of quasi-independence.

Suggested Citation

  • Jay Magidson & James H. Swan & Richard A. Berk, 1981. "Estimating Nonhierarchical and Nested Log-Linear Models," Sociological Methods & Research, , vol. 10(1), pages 3-49, August.
    • Handle: RePEc:sae:somere:v:10:y:1981:i:1:p:3-49
    DOI: 10.1177/004912418101000106
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/004912418101000106
    Download Restriction: no
    File URL: https://libkey.io/10.1177/004912418101000106?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
    ---><---

    References listed on IDEAS

    as
    1. Jay Magidson, 1975. "Qualitative Variables and Simultaneous Equation Econometric Models," Discussion Papers 142, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    Full references (including those not matched with items on IDEAS)

    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. Daniel L. McFadden, 1976. "Quantal Choice Analysis: A Survey," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 363-390, National Bureau of Economic Research, Inc.

    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:sae:somere:v:10:y:1981:i:1:p:3-49. 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: SAGE Publications (email available below). General contact details of provider: .

    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.