For instance, A | B | C is evaluated as follows: The variables on the right- and left-hand sides of the bar become effects, and the cross of them becomes an effect. When the bar (|) is used, the expression on the right side of the equal sign is expanded from left to right by using the equivalents of rules 2–4 given in Searle ( 1971, p. 390). Note that no asterisks appear within the nested list or immediately before the left parenthesis. The crossed list should come first, followed by the nested list in parentheses. The general form of an effect can be illustrated by using the CLASS variables A, B, C, D, E, and F: Nested effects test hypotheses similar to interactions, but the levels of the nested variables are not the same for every combination within which they are nested. The main effect or crossed effect is nested within the effects listed in parentheses: B( A) C* D( A B). Nested effects are specified by following a main effect or crossed effect with a CLASS variable or list of CLASS variables enclosed in parentheses in the MODEL statement. Interaction terms in a model test the hypothesis that the effect of a factor does not depend on the levels of the other factors in the interaction. Main effects used as independent variables test the hypothesis that the mean of the dependent variable is the same for each level of the factor in question, ignoring the other independent variables in the model.Ĭrossed effects (interactions) are specified by joining the CLASS variables with asterisks in the MODEL statement: A* B A* C A* B* C. Main effects are specified by writing the variables by themselves in the CLASS statement: A B C. The analysis-of-variance model specifies effects, which are combinations of classification variables used to explain the variability of the dependent variables in the following manner: This is in contrast to the response (or dependent) variables, which are continuous. Classification variables can be either numeric or character. The values of a classification variable are called levels. Classification variables are also called categorical, qualitative, discrete, or nominal variables. In SAS analysis-of-variance procedures, the variables that identify levels of the classifications are called classification variables, and they are declared in the CLASS statement.
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