What is Categorical Outcome Variable

Microeconomic datasets are usually large, mainly survey data. These data are samples of hundreds of respondents or group of respondents (e.g., households). The distributions of such data are mostly not normal because some responses/variables are discrete. Handling such datasets poses some problems of summarizing/reporting the important features of the data in estimations. This study concentrates on how to handle categorical variables in estimation/reporting based on theoretical and empirical knacks. This study used Ghana Demographic and Health Survey data for 2014 for illustration and elaborates on how to interpret results of binary and multinomial outcome regressions. Comparison is made on the different binary models, and binary logit is found to be weighted over the other binary models. Multinomial logistic model is best handled when the odds of one outcome versus the other outcome are independent of other outcome alternatives as verified by the Independent of Irrelevant Alternatives (IIA). Conclusions and suggestions for handling categorical models are discussed in the study.