PLS Introductory Overview - Types of Analyses

The design for an analysis can include effects for continuous as well as categorical predictor variables. Designs may include polynomials for continuous predictors (e.g., squared or cubic terms) as well as interaction effects (i.e., product terms) for continuous predictors. For categorical predictor variables, one can fit ANOVA-like designs, including full factorial, nested, and fractional factorial designs, etc. Designs can be incomplete (i.e., involve missing cells), and effects for categorical predictor variables can be represented using either the sigma-restricted parameterization or the overparameterized (i.e., indicator variable) representation of effects.

The topics below give complete descriptions -- in the context of the STATISTICA General Linear Model (GLM) module -- of the types of designs that can be analyzed using partial least squares regression, as well as types of designs that can be analyzed using the general linear model.

Between-subject designs