Workspace Node: GLM Custom Design - Specifications - Options Tab

In the GLM Custom Design node dialog box, under the Specifications heading, select the Options tab to access the following options.

Element Name Description
Random factors Click the Random factors button to display the Random Effects (Mixed Model) dialog box, which will contain the currently selected categorical predictor variables (factors). Choose the categorical variables (factors) that represent random factors in the design. Note that in the analysis, all interaction effects involving random factor effects will be treated as random effects. For a discussion of random effects, and the estimation of variance components, see the Introductory Overview, or the Variance Components and Mixed-Model ANOVA/ANCOVA topics.
Sweep Delta Enter the negative exponent for a base-10 constant delta (delta = 10-sdelta) in the Sweep Delta field; the default value is 7. Delta is used (1) in sweeping, to detect redundant columns in the design matrix, and (2) for evaluating the estimability of hypotheses; specifically a value of 2*delta is used for the estimability check.
Inverse Delta Enter the negative exponent for a base-10 constant delta (delta = 10-idelta) in the Inverse Delta field; the default value is 12. Delta for matrix inversion is used to check for matrix singularity in matrix inversion calculations.
Parameterization In this group box to specify the type of parameterization to use for the general linear model.
Sigma-restricted Select this check box to compute the design matrix for categorical predictors in the model based on sigma-restricted coding; if it is not selected, the overparameterized model will be used. The sigma-restricted model is the default parameterization, except for models that involve nested or random effects; see the Sigma-Restricted vs. Overparameterized Model topic for details.
No intercept Select this check box to exclude the intercept from the model.
Lack of fit Select this check box to compute the sums of squares for the pure error, i.e., the sums of squares within all unique combinations of values for the (continuous and categorical) predictor variables. On the GLM Results tabs, options are available to test the lack-of-fit hypothesis.
Cross-validation Click this button to display the Cross-validation dialog box for specifying a categorical variable and a (code) value to identify observations that should be included in the computations for fitting the model (the analysis sample); all other observations with valid data for all predictor variables and dependent variables will automatically be classified as belonging to the validation sample (see the GLM Results - Residuals 1 tab topic for a description of the available residual statistics for observations in the validation sample); note that all observations with valid data for all predictor variables but missing data for the dependent variables will automatically be classified as belonging to the prediction sample (see the Residuals tab topic for a description of available statistics for the prediction sample).
Sums of squares In this group box, select the method for constructing main effect and interaction hypotheses in unbalanced and incomplete designs; these methods are discussed in detail in the Six types of sums of squares topic. For the sigma-restricted model, the default value is Type VI (unique or effective hypothesis decomposition; see Hocking, 1985) and Type IV is not valid; for the overparameterized model the default value is Type III (orthogonal; see Goodnight, 1980), and Type VI is not valid. See also General ANOVA/MANOVA and GLM Notes - Sums of squares for additional details.

Options / C / W. See Common Options.

OK Click the OK button to accept all the specifications made in the dialog box and to close it. The analysis results will be placed in the Reporting Documents node after running (updating) the project.