Chapter 10 Categorical Data Analysis

LEARNING OUTCOMES

• Distinguish between the different types of categorical data and their underlying sampling distributions.
• Identify the statistical tests proposed for categorical data analysis of independent and matched data.
• Apply R's functions to estimate the parameters of categorical models and evaluate the R code and appraise the outputs.
• Produce contingency tables and data visualizations to summarize categorical models.

A categorical variable assigns numerical values to a set of discrete categories. The measurement level of categorical data ranges from assigning two values to two distinct categories (binomial data or dichotomous variables), to assign values to more than two unordered categories (multinomial data, nominal data, or polytomous variables), to assign values to more than two ordered categories (ordinal data or ordinal variables).

In some cases, we do not have frequency data from independent units or individuals, but a number of events that are not independent.

TODO! provide example of counts.