Categorical Data Analysis
Modeling and inferential procedures (estimation and precision assessment) are discussed for a single proportion, thereafter in the context of cross classified data, i.e. contingency tables, is discussed. First, the situation of two by two tables is given some treatment and then the more general context of R by C tables is considered. Particular emphasis is put on testing the null hypothesis of no association (independence) between the row and column classification. Next, the setting of multiple contingency tables is considered. Attention is given to the log linear model and to the multinomial regression model. When the covariate is continuous, these methods naturally extend to logistic regression, a method at the intersection of generalized linear modeling and categorical data analysis. When the outcome is a count, one often uses Poisson regression. Finally, correlated categorical data settings are briefly discussed.
International Encyclopedia of Statistical Science