Which ANOVA design accommodates repeated measurements on the same subjects?

Which ANOVA design accommodates repeated measurements on the same subjects?

• A. ANOVA for repeated measures designs
• B. Two-way ANOVA
• C. One-way ANOVA
• D. MANOVA

Answer: (A)

When you measure the same people multiple times, the observations within each subject are related. A design that specifically handles this within-subject correlation is the ANOVA for repeated measures designs. It separates variability due to differences between subjects from variability due to changes across the conditions or time points within the same subjects, which gives a clearer test of whether the factor of interest has an effect. This approach also increases statistical power by removing a lot of the between-subject variability from the error term. It relies on the same participants across all levels of the manipulated factor, and it brings in considerations like sphericity; if that assumption isn’t met, corrections (like Greenhouse-Geisser or Huynh-Feldt) are used to adjust the degrees of freedom. Other designs don’t inherently account for repeated measurements on the same individuals. A two-way ANOVA examines two factors and can be between-subjects or mixed, but it isn’t specifically built for repeated measures on the same subjects. A one-way ANOVA looks at a single factor with independent groups. MANOVA handles multiple dependent variables at once, not the within-subject correlation structure created by repeated measurements.

When you measure the same people multiple times, the observations within each subject are related. A design that specifically handles this within-subject correlation is the ANOVA for repeated measures designs. It separates variability due to differences between subjects from variability due to changes across the conditions or time points within the same subjects, which gives a clearer test of whether the factor of interest has an effect.

This approach also increases statistical power by removing a lot of the between-subject variability from the error term. It relies on the same participants across all levels of the manipulated factor, and it brings in considerations like sphericity; if that assumption isn’t met, corrections (like Greenhouse-Geisser or Huynh-Feldt) are used to adjust the degrees of freedom.

Other designs don’t inherently account for repeated measurements on the same individuals. A two-way ANOVA examines two factors and can be between-subjects or mixed, but it isn’t specifically built for repeated measures on the same subjects. A one-way ANOVA looks at a single factor with independent groups. MANOVA handles multiple dependent variables at once, not the within-subject correlation structure created by repeated measurements.