- Syllabus - Introduction to Stata - Lecture Slides - Lecture do-files- Assignments- Stata Setup do-fileCourse DescriptionCategorical Data Analysis is a course in applied statistics that primarily deals with regression models in which the dependent variable is binary, nominal, ordinal, or count. In addition, some flexible methods for nonlinearities within the linear regression framework will be briefly covered. Many common statistical issues encountered by social scientists require different methods when the dependent variable is not continuous. E.g. Interpretation of coefficients, calculation of predictions, testing of interaction effects, testing for mediation, assessing model fit, and many other techniques require a different approach for categorical dependent variables than those for continuous outcomes. The focus of the course is on interpretation and learning to deal with the complications introduced by the nonlinearity of the models. Less focus will be on the mathematical details of the models except where pertinent. Specific models considered include: probit and logit for binary outcomes; ordered logit, ordered probit, and alternating least squares optimal scaling for ordinal outcomes; multinomial logit for nominal outcomes; Poisson, negative binomial, and zero inflated models for counts; and fractional response, LOWESS, and local polynomial smoothing methods for continuous and quasi-continuous outcomes. |

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