Interpreting the Parameters of Log-Linear Models

Abstract

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.

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