Diagnostics in Categorical Data Analysis

Abstract

SUMMARY Diagnostics as measures of model deviations and of the influence of particular data sets are used extensively in modern regression analysis. For contingency tables, and more generally for the parametric multinomial distribution, it is not the influence of individual observations which is of interest, but rather the contribution to a lack of model fit or to the values of the parameter estimates from a single cell in the table, which must be evaluated. Hence diagnostics for contingency tables take somewhat different forms. In this paper such diagnostics are discussed and their applicability is illustrated with data sets analysed by the Goodman RC association model.