What characterizes a multi-factorial ANOVA?

What characterizes a multi-factorial ANOVA?

• A. More than one independent variable is analyzed
• B. A single factor with multiple levels
• C. Repeated measures design
• D. A non-parametric alternative to ANOVA

Answer: (A)

A multi-factorial ANOVA is defined by analyzing more than one independent variable at the same time, with the design crossing the factors so that every combination of their levels is represented. This allows you to examine the main effects of each factor—how each variable affects the dependent variable on average—and to test for interactions, meaning whether the effect of one factor depends on the level of another. If there’s only a single factor with multiple levels, you’d be doing a one-way ANOVA rather than a factorial one. Repeated measures refers to within-subjects data and isn’t the defining feature of a factorial design, and a non-parametric alternative would be used when ANOVA assumptions aren’t met, not to characterize the design itself.

A multi-factorial ANOVA is defined by analyzing more than one independent variable at the same time, with the design crossing the factors so that every combination of their levels is represented. This allows you to examine the main effects of each factor—how each variable affects the dependent variable on average—and to test for interactions, meaning whether the effect of one factor depends on the level of another. If there’s only a single factor with multiple levels, you’d be doing a one-way ANOVA rather than a factorial one. Repeated measures refers to within-subjects data and isn’t the defining feature of a factorial design, and a non-parametric alternative would be used when ANOVA assumptions aren’t met, not to characterize the design itself.