Fitting Log-Linear Models to Contingency Tables From Surveys With Complex Sampling Designs: An Investigation of the Clogg-Eliason Approach

Abstract

Clogg and Eliason (1987) proposed a simple method for taking account of survey weights when fitting log-linear models to contingency tables. This article investigates the properties of this method. A rationale is provided for the method when the weights are constant within the cells of the table. For more general cases, however, it is shown that the standard errors produced by the method are invalid, contrary to claims in the literature. The method is compared to the pseudo maximum likelihood method both theoretically and through an empirical study of social mobility relating daughter’s class to father’s class using survey data from France. The method of Clogg and Eliason is found to underestimate standard errors systematically. The article concludes by recommending against the use of this method, despite its simplicity. The limitations of the method may be overcome by using the pseudo maximum likelihood method.

Keywords

complex sampling jackknife log linear model pseudo maximum likelihood stratification survey weight

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