General Linear Models Button

Click the button to display the General Linear Models (GLM) Startup Panel. The General Linear Models (GLM) module provides a generalization of the linear regression model, such that effects can be tested 1) for categorical predictor variables, as well as for effects for continuous predictor variables and 2) in designs with multiple dependent variables as well as in designs with a single dependent variable.

GLM is a complete implementation of the general linear model. You can choose simple or highly customized one-way, main-effect, factorial, or nested ANOVA or MANOVA designs, repeated measures designs, simple, multiple and polynomial regression designs, response surface designs (with or without blocking), mixture surface designs, simple or complex analysis of covariance designs (e.g., with separate slopes), or general multivariate MANCOVA designs. Factors can be fixed or random (in which case synthesized error terms will be computed). GLM offers both the overparameterized and Sigma-restricted parameterization for categorical factor effects. STATISTICA will compute the customary Type I through IV sums of squares for unbalanced and incomplete designs; GLM also offers two additional methods for analyzing missing cell designs: Hockings (1985) "effective hypothesis decomposition," and a method that will automatically drop effects that cannot be fully estimated (e.g., when the least squares means do not exist for all levels of the respective main effect or interaction effect). The latter method is the one commonly applied to the analysis of highly fractionalized designs in industrial experimentation (see also Experimental Design). Results statistics computed by GLM include ANOVA tables with univariate and multivariate tests, descriptive statistics, a comprehensive selection of different types of plots of means (observed, least squares, weighted) for higher-order interactions, with error bars (standard errors) for effects involving between-group factors as well as repeated measures factors; extensive residual analyses and plots (for the "training" or computation sample, for a cross-validation or "verification" sample, or for a prediction sample), desirability profiling, specifications of custom error terms and effects; comprehensive post-hoc comparison methods for between-group effects as well as repeated measures effects, and the interactions between repeated measures and between effects including: Fisher LSD, Bonferroni, Scheffé, Tukey HSD, Unequal N HSD, Newman Keuls, Duncan, and Dunnett's test (with flexible options for estimating the appropriate error terms for those tests), tests of assumptions (e.g., Levene's test, plots of means vs. standard deviations, etc.).

The General Regression Models (GRM) module offers methods for stepwise and best-subset selection of effects in a general linear model; see also the Generalized Linear/Nonlinear Models (GLZ) module for nonlinear alternatives to GLM.