Categorical Data Analysis

This is the github repo for the web pages of my course, Psychology 6136: Categorical Data Analysis, a graduate-level course taught at York University.

It uses my book, Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data, and accompanying web site http://ddar.datavis.ca.

The web materials for this course are hosted on https://friendly.github.io/psy6136. A previous version of this course was at https://www.yorku.ca/friendly/psy6136/, now obsolete.

This course is designed as a broad, applied introduction to the statistical analysis of categorical (or discrete) data, such as counts, proportions, nominal variables, ordinal variables, discrete variables with few values, continuous variables grouped into a small number of categories, etc.

• The course begins with methods designed for cross-classified table of counts, (i.e., contingency tables), using simple chi square-based methods.

• It progresses to generalized linear models, for which log-linear models provide a natural extension of simple chi square-based methods.

• This framework is then extended to comprise logit and logistic regression models for binary responses and generalizations of these models for polytomous (multicategory) outcomes.

Throughout, there is a strong emphasis on associated graphical methods for visualizing categorical data, checking model assumptions, etc. Lab sessions will familiarize the student with software using R for carrying out these analyses.