Categorical Data Analysis – Statistical Horizons Seminar
Next offering: June 14 - 17
Remote seminar offered synchronously (with asynchronous participation allowed).
Read details about and/or register to attend this seminar on the Statistical Horizons site.
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 a 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 including interpretation of coefficients, calculation of predictions, testing of interaction effects, testing for mediation or other cross-model comparisons, and assessing model fit, require a different approach for models with categorical dependent variables. 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.
Why can’t I use OLS for all dependent variables?
Nonlinear effects, interaction effects, and nonlinear interaction effects
Binary dependent variables: logit and probit models
Take-home data analysis assignment #1 (optional): binary DV models
Interpreting categorical dependent variable models: coefficients, multiplicative effects, predictions, marginal effects, and visualizations
Count dependent variables: Poisson and negative binomial models
Zero-inflated count models
Nominal dependent variables: multinomial logit models
Ordinal models: ordinal logit and probit, generalized ordered logit models
Take-home data analysis assignment #2 (optional): nominal and ordinal DV models
Interaction / moderation for categorical models
Comparing predictions and effects across categorical models (e.g. mediation)
Absolute and comparative model fit for categorical models
Model diagnostics for categorical models