Complex Designs - Incomplete (Nested) Designs
There are instances where we may decide to ignore interaction effects. This happens when (1) we know that in the population the interaction effect is negligible, or (2) when a complete factorial design (this term was first introduced by Fisher, 1935a) cannot be used for economic reasons. Imagine a study where we want to evaluate the effect of four fuel additives on gas mileage. For our test, our company has provided us with four cars and four drivers. A complete factorial experiment, that is, one in which each combination of driver, additive, and car appears at least once, would require 4 x 4 x 4 = 64 individual test conditions (groups). However, we may not have the resources (time) to run all of these conditions; moreover, it seems unlikely that the type of driver would interact with the fuel additive to an extent that would be of practical relevance. Given these considerations, one could actually run a so-called Latin square design and "get away" with only 16 individual groups (the four additives are denoted by letters A, B, C, and D):
Latin square designs (this term was first used by Euler, 1782) are described in most textbooks on experimental methods (e.g., Hays, 1988; Lindman, 1974; Milliken & Johnson, 1984; Winer, 1962), and we do not want to discuss here the details of how they are constructed. Suffice it to say that this design is incomplete insofar as not all combinations of factor levels occur in the design. For example, Driver 1 will only drive Car 1 with additive A, while Driver 3 will drive that car with additive C. In a sense, the levels of the additives factor (A, B, C, and D) are placed into the cells of the Car by Driver matrix like "eggs into a nest." This mnemonic device is sometimes useful for remembering the nature of nested designs. The facilities in ANOVA/MANOVA allow you to easily analyze these types of designs, by specifying them as Main effects ANOVA designs. However, when the nesting is complex, or a factorial design is nested inside the factors of one or more other factors, then you need to use GLM to "construct proper hypotheses" about the data, i.e., to analyze the design. See also the GLM topic Nested ANOVA Designs for additional details.
Note that there are several other modules of STATISTICA that can also analyze these types of designs; see the section on Methods for Analysis of Variance for details. In particular the Variance Components and Mixed Model ANOVA/ANCOVA module is very efficient for analyzing designs with unbalanced nesting (when the nested factors have different numbers of levels within the levels of the factors in which they are nested), very large nested designs (e.g., with more than 200 levels overall), or hierarchically nested designs (with or without random factors).