Statistics - Advanced Linear/Nonlinear Models - Log-linear Analysis of Frequency Tables

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select Log-Linear to display the Log-Linear Analysis Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models submenu, select Log-Linear Analysis of Frequency Tables to display the Log-Linear Analysis Startup Panel.

Log-linear analysis provides a "sophisticated" way of looking at crosstabulation tables (to explore the data or verify specific hypotheses), and it is sometimes considered an equivalent of ANOVA for frequency data. Specifically, it is used to test the different factors that are used in the crosstabulation (e.g., gender, region, etc.) and their interactions for statistical significance.

The Log-Linear Analysis module offers a complete implementation of log-linear modeling procedures for multi-way frequency tables. Frequency tables can be computed from raw data, or can be entered directly into STATISTICA. You can, at all times, review the complete observed table as well as marginal tables, and fitted (expected) values, and can evaluate the fit of all partial and marginal association models or select specific models (marginal tables) to be fitted to the observed data. STATISTICA also offers an intelligent automatic model selection procedure that first determines the necessary order of interaction terms required for a model to fit the data, and then, through backwards elimination, determines the best sufficient model to satisfactorily fit the data (using criteria determined by you). The standard output includes G-square (Maximum-Likelihood Chi-square), the standard Pearson Chi-square with the appropriate degrees of freedom and significance levels, the observed and expected tables, marginal tables, and other statistics. Graphics options available in the Log-Linear module include a variety of 2D and 3D graphs designed to visualize 2-way and multi-way frequency tables (including interactive, user-controlled cascades of categorized histograms and 3D histograms revealing "slices" of multi-way tables), plots of observed and fitted frequencies, plots of various residuals (standardized, components of Maximum-Likelihood Chi-square, Freeman-Tukey deviates, etc.), and many others.

See also the Generalized Linear/Nonlinear Models (GLZ) module, which provides options for analyzing binomial and multinomial logit models with coded ANOVA/ANCOVA-like designs.