Abstract

This article addresses the issue of misclassification in a single categorical variable, that is, how to test whether the collected categorical data are misclassified.  To tackle this issue, a pair of null and alternative hypotheses is proposed. A mixed Bayesian approach is taken to test these hypotheses. Specifically, a bias-adjusted cell proportion estimator is presented that accounts for the bias caused by classification errors in the observed categorical data. The chi-square test is then adjusted accordingly. To test the null hypothesis that the data are not misclassified under a specified multinomial distribution against the alternative hypothesis they are misclassified, the Bayes factor is calculated for the observed data and a comparison is made with the classical p-value. 

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