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Interpreting the Parameters of Log-Linear Models



    (State University of New York at Albany)


This article develops a straightforward approach to the interpretation of the parameters in log-linear models through a detailed consideration of examples. The focus is on models involving dependent variables, and the conventions of regression analysis are used to represent variables in models for the logit, or logarithm of the odds of the dependent variable. By simple manipulations, the parameters in logit models are related to such quantities as odds and odds ratios. The magnitudes of the effects conveyed by the parameters are also described in terms of typical percentage differences. The examples considered include models with complex interaction structures and for polytomous dependent variables. The mechanics of developing parameter estimates from the output of widely available computer programs are shown.

Suggested Citation

  • Richard D. Alba, 1987. "Interpreting the Parameters of Log-Linear Models," Sociological Methods & Research, , vol. 16(1), pages 45-77, August.
  • Handle: RePEc:sae:somere:v:16:y:1987:i:1:p:45-77

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    Cited by:

    1. Paas, L.J. & Bijmolt, T.H.A. & Vermunt, J.K., 2004. "Extending dynamic segmentation with lead generation : A latent class Markov analysis of financial product portfolios," Discussion Paper 2004-1, Tilburg University, Center for Economic Research.
    2. Richard Alba & John Logan, 1991. "Variations on two themes: Racial and ethnic patterns in the attainment of suburban residence," Demography, Springer;Population Association of America (PAA), vol. 28(3), pages 431-453, August.

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