Systematically Misclassified Binary Dependant Variables

When a binary dependant variable is misclassified probit and logit estimates are biased and inconsistent. In this paper we suggest a conceptual basis for endogenous misclassification of the dependant variable due to systematic respondent bias and the use of Likert scales commonly used in measuring categorical variables. We develop an estimation approach that corrects for endogenous misclassification, validate our approach using a simulation study and apply it to the analysis of a treatment program designed to improve family dynamics. Our results show that endogenous misclassification could lead towards potentially incorrect conclusions unless corrected using an appropriate technique.