Contingency tables analysis is a central branch of categorical data analysis, and is focused on the analysis of data represented as contingency tables. This sort of analysis includes hypothesis testing as well estimation of model parameters, e.g. applying loglinear regression methods to fit loglinear models to the data.
Contingency tables analysis is widely used in marketing research, in biomedical research, including drug trials, as well as in the social sciences.
The major classes of questions addressed by contingency tables analysis are
i) a hypothesis testing question of whether there is association among the variables, or whether the variables are independent? For example, in a drug trial, dependence between “Outcome” (e.g. “Improvement”, “No change”, “Worsening”) and treatment received (e.g “No treatment”, “Drug A”) would signify that the drug has an effect on patient outcome.
ii) Which model provides the best explanation for the data at hand?