Used to compare multiple measures from the same group

Used to compare multiple measures from the same group

• A. Chi Square
• B. ANOVA
• C. T-test
• D. Repeated measures ANOVA

Answer: (D)

When you want to compare several measurements taken from the same group, you need a method that accounts for the fact that observations are linked within each person. Repeated measures ANOVA does this by treating each subject as contributing multiple related observations across conditions or time points, and it separates variability due to differences between people from variability due to the conditions themselves. This lets you test whether mean values differ across all measured points while properly handling the within-subject correlations. If you only have two measurements, a paired t-test could work, but for three or more, repeated measures ANOVA uses all the data efficiently and controls the risk of false positives from multiple comparisons. A key assumption is sphericity, and when that is violated you can apply corrections like Greenhouse-Geisser. In contrast, chi-square is for categorical data, a standard ANOVA assumes independent groups, and a t-test compares two means—none of those handle multiple related measures from the same individuals as effectively.

When you want to compare several measurements taken from the same group, you need a method that accounts for the fact that observations are linked within each person. Repeated measures ANOVA does this by treating each subject as contributing multiple related observations across conditions or time points, and it separates variability due to differences between people from variability due to the conditions themselves. This lets you test whether mean values differ across all measured points while properly handling the within-subject correlations. If you only have two measurements, a paired t-test could work, but for three or more, repeated measures ANOVA uses all the data efficiently and controls the risk of false positives from multiple comparisons. A key assumption is sphericity, and when that is violated you can apply corrections like Greenhouse-Geisser. In contrast, chi-square is for categorical data, a standard ANOVA assumes independent groups, and a t-test compares two means—none of those handle multiple related measures from the same individuals as effectively.