IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v61y2020i1d10.1007_s00362-017-0936-2.html
   My bibliography  Save this article

Log-linear models to evaluate direction of effect in binary variables

Author

Listed:
  • Wolfgang Wiedermann

    (University of Missouri)

  • Alexander Eye

    (Michigan State University)

Abstract

In this article, we present a new approach to determining direction of effects in binary variables. A variable is considered explanatory if it explains the probability distribution of another variable. In cross-classifications of binary variables, the univariate probability distribution of variables can be considered explained if omitting the univariate effects of this variable does not lead to an ill-fitting model. Directional (non-hierarchical) log-linear models are introduced that allow statements concerning the direction of association in binary data. Cases in which variables of latent linear regression processes are partially observed as binary variables reveal a close conceptual link between the proposed log-linear approach and existing direction of effect methodology for metric variables. A Monte Carlo study is presented that shows that the proposed approach has good power and enables researchers to distinguish the correct model from the incorrect, reverse model. The approach can be extended to multiple explanatory and multiple outcome variables. Empirical data examples from research on aggression development in adolescence illustrate the proposed direction dependence approach.

Suggested Citation

  • Wolfgang Wiedermann & Alexander Eye, 2020. "Log-linear models to evaluate direction of effect in binary variables," Statistical Papers, Springer, vol. 61(1), pages 317-346, February.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:1:d:10.1007_s00362-017-0936-2
    DOI: 10.1007/s00362-017-0936-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-017-0936-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00362-017-0936-2?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. A. Gupta & T. Nguyen & L. Pardo, 2007. "On Christensen's conjecture," Statistical Papers, Springer, vol. 48(3), pages 523-523, September.
    2. K. Gupta & T. Nguyen & L. Pardo, 2007. "On Christensen's conjecture," Statistical Papers, Springer, vol. 48(2), pages 313-319, April.
    3. J. A. Nelder, 1974. "Log Linear Models for Contingency Tables: A Generalization of Classical Least Squares," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(3), pages 323-329, November.
    4. Yadolah Dodge & Iraj Yadegari, 2010. "On direction of dependence," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(1), pages 139-150, July.
    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. Liu, Zijun & Chen, Sheng & Tang, Tingting & Luo, Hang & Guan, Qinghao, 2024. "How public education investment and advanced human capital structure affect regional innovation: A spatial econometric analysis from the perspective of innovation value chain," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).

    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. Martín, Nirian, 2015. "Diagnostics in a simple correspondence analysis model: An approach based on Cook’s distance for log-linear models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 175-189.
    2. J. Scott Long, 1984. "Estimable Functions in Log-Linear Models," Sociological Methods & Research, , vol. 12(4), pages 399-432, May.
    3. A Pickles, 1980. "Models of Movement: A Review of Alternative Methods," Environment and Planning A, , vol. 12(12), pages 1383-1404, December.
    4. Fischer, M.M. & Nijkamp, P., 1985. "Explanatory discrete spatial data and choice analysis : a state-of-the-art review," Serie Research Memoranda 0006, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    5. Keith Hope, 1983. "On Defining Normative Consistency," Sociological Methods & Research, , vol. 12(1), pages 95-111, August.

    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:stpapr:v:61:y:2020:i:1:d:10.1007_s00362-017-0936-2. 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.