Categorical Data Analysis – January 2022

Event Phone: 1-610-715-0115

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A 3-Day Remote Seminar
Taught by Trenton Mize, Ph.D.

Many—perhaps even most— behavioral, health, and social science questions include outcome variables that are categorical. E.g. Which political candidate will win the next election? How does parent’s social class influence children’s educational attainment? How many publications does it take to receive tenure? Do men or women drink more alcoholic drinks? Is a vaccine effective at preventing disease? Answering these—and countless other—questions cannot be adequately accomplished via the linear regression model and instead require the more advanced techniques covered extensively in this seminar.

Categorical Data Analysis is a seminar in applied statistics that primarily deals with regression models in which the dependent variable is binary, nominal, ordinal, or count. 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 or other cross-model comparisons, assessing model fit, and many other techniques require a different approach for models of categorical dependent variables compared to the methods used for linear regression. The focus of the course is on interpretation and learning to deal with the complications introduced by the nonlinearity of the models.

Specific models considered include: probit and logit for binary outcomes; ordered logit/probit and the generalized ordered logit model for ordinal outcomes; multinomial logit for nominal outcomes; and Poisson, negative binomial, and zero inflated models for counts.

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