ABSTRACT
One basic and straightforward method for analyzing categorical data is via
crosstabulation. For example, a medical researcher may tabulate the frequency of
different symptoms by patient’s age and gender; an educational researcher may tabulate the number of high school drop-outs by age, gender, and ethnic back-
ground; an economist may tabulate the number of business failures by industry,
region, and initial capitalization; a market researcher may tabulate consumer
preferences by product, age, and gender, etc. In all of these cases, the most in-
teresting results can be summarized in a multiway frequency table, that is, in a
crosstabulation table with two or more factors. Loglinear models provide a more
“sophisticated” way of looking at crosstabulation tables. Specifically, one can
test the different factors that are used in the crosstabulation (e.g., gender, region, etc.) and their interactions for statistical significance. In this introductory
section we present an intuitive approach to loglinear models and in the remaining
sections a systematic study of them.
