ABSTRACT
The first characteristic of the two-factor analysis of variance (ANOVA) model should be obvious; this model considers the effect of two factors or independent variables on one dependent variable. This chapter is concerned with two- and three-factor models, but the extension to more than three factors, when warranted, is fairly simple. It describes the distinguishing characteristics of the two-factor ANOVA model, the layout of the data, the linear model, main effects and interactions, assumptions of the model and their violation, partitioning the sums of squares, the ANOVA summary table, multiple comparison procedures, effect size measures, confidence intervals, power, an example, and expected mean squares. Factorial ANOVA models with more than two independent variables will, accordingly, test for additional main effects and interactions. For factorial ANOVA, the distributional shape for the residuals should be a normal distribution.
