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.
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The course begins with methods designed for cross-classified table of counts, (i.e., contingency tables), using simple chi square-based methods.
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It progresses to generalized linear models, for which log-linear models provide a natural extension of simple chi square-based methods.
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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.