<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Categorical Dependent Variables Estimations With Some\r\nEmpirical Applications"^^ . "Microeconomic datasets are usually large, mainly survey data. These data are samples of hundreds of respondents\r\nor group of respondents (e.g., households). The distributions of such data are mostly not normal because some\r\nresponses/variables are discrete. Handling such datasets poses some problems of summarizing/reporting the\r\nimportant features of the data in estimations. This study concentrates on how to handle categorical variables in\r\nestimation/reporting based on theoretical and empirical knacks. This study used Ghana Demographic and Health\r\nSurvey data for 2014 for illustration and elaborates on how to interpret results of binary and multinomial outcome\r\nregressions. Comparison is made on the different binary models, and binary logit is found to be weighted over the\r\nother binary models. Multinomial logistic model is best handled when the odds of one outcome versus the other\r\noutcome are independent of other outcome alternatives as verified by the Independent of Irrelevant Alternatives (IIA).\r\nConclusions and suggestions for handling categorical models are discussed in the study."^^ . "2020" . . . "Applied Econometric Analysis: Emerging Research and Opportunities"^^ . . . . . . . . . . . "E. S. C."^^ . "Osabuohien"^^ . "E. S. C. Osabuohien"^^ . . "Alhassan A."^^ . "Karakara"^^ . "Alhassan A. Karakara"^^ . . . . . . "Categorical Dependent Variables Estimations With Some\r\nEmpirical Applications (PDF)"^^ . . . "Categorical Dependent.pdf"^^ . . "HTML Summary of #17781 \n\nCategorical Dependent Variables Estimations With Some \nEmpirical Applications\n\n" . "text/html" . . . "H Social Sciences (General)"@en . . . "HB Economic Theory"@en . . . "ZA Information resources"@en . . . . . . . . . "E. S. C."^^ . "Osabuohien"^^ . "E. S. C. Osabuohien"^^ . ""^^ . . "Alhassan A."^^ . "Karakara"^^ . "Alhassan A. Karakara"^^ . ""^^ .