Log-Linear Analysis Overview
One basic and straightforward method for analyzing data is via crosstabulation.
For example, a medical researcher may tabulate the frequency of different symptoms by patients' age and gender; an educational researcher may tabulate the number of high school drop-outs by age, gender, and ethnic background; 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 these cases, the major results of interest can be summarized in a multi-way frequency table, that is, in a crosstabulation table with two or more factors.
Statistica contains a designated module, Basic Statistics, for generating, analyzing, and reviewing different types of tables. Log-Linear Analysis provides a more sophisticated way of looking at crosstabulation tables. Specifically, this module allows the user to test the different factors that are used in the crosstabulation (such as gender, region.) and their interactions for statistical significance (see Elementary Concepts for a discussion of statistical significance testing). The following text will present a brief introduction to these methods, their logic, and interpretation.
Statistica also includes a designated module for performing correspondence analysis for two-way and multi-way frequency tables (for example, to perform Multiple Correspondence Analysis). Correspondence analysis is a descriptive/exploratory technique designed to analyze two-way and multi-way tables containing some measure of correspondence between the rows and columns. The results provide information that is similar in nature to those produced by Factor Analysis techniques, and make it possible to explore the structure of the categorical variables included in the table.