Appendix B
Log-linear Model—An Introduction
(Source: Knoke and Burke [1] and Course Material by Angela Jeansonne)
Log-linear analysis is an extension of the two-way contingency table where the conditional relationship between two or more discrete, categorical variables is analyzed by taking the natural logarithm of the cell frequencies within a contingency table. The log-linear model states the expected cell frequencies of a cross-tabulation (the 
) as functions of parameters representing characteristics of the categorical variables and their relationships with each other. The general log-linear model does not distinguish between independent and dependent variables. All variables are treated alike as ‘response variables’ whose mutual associations are explored.
The usual data suitable for log-linear analysis are contingency tables. Angela Jeansonne provided an example in the class materials. Suppose we are interested in the relationship between sex, heart disease, and body weight. We could take a sample of 200 subjects and determine the sex, approximate body weight, and who does and does not have heart disease. The continuous variable, body weight, is broken down into two discrete categories: not overweight and overweight. The contingency table containing the data may look like this:
Angela Jeansonne in the class material states that the basic strategy in log-linear modeling involves ...
