Which statistical method is appropriate for handling repeated measures with specific independent groups?

Which statistical method is appropriate for handling repeated measures with specific independent groups?

• A. Mixed ANOVA
• B. Single-factor ANOVA
• C. Mann-Whitney U test
• D. Paired t-test

Answer: (A)

The mixed ANOVA is the correct choice for handling repeated measures with specific independent groups because it allows for the examination of both within-subject (repeated measures) and between-subject (independent groups) variables simultaneously. This statistical method is particularly useful when comparing the effects of different treatments or conditions on the same subjects across multiple time points, as well as measuring differences across different groups. In the context of a mixed ANOVA, the repeated measures involve observations taken on the same subjects under different conditions (e.g., measurements taken at different time points), while the independent groups allow the researcher to analyze how these measures may differ across distinct groups (e.g., a treatment group versus a control group). This comprehensive approach is vital in psychological research where understanding both individual variability and group differences is important. Other statistical methods mentioned are not suitable for this specific scenario. Single-factor ANOVA only examines one independent variable's effect across groups but does not account for repeated measures on the same subjects. The Mann-Whitney U test is a non-parametric test used for comparing differences between two independent groups but does not apply to situations involving repeated measures. The paired t-test is ideal for comparing two related groups but is limited to situations where there are only two related samples,

The mixed ANOVA is the correct choice for handling repeated measures with specific independent groups because it allows for the examination of both within-subject (repeated measures) and between-subject (independent groups) variables simultaneously. This statistical method is particularly useful when comparing the effects of different treatments or conditions on the same subjects across multiple time points, as well as measuring differences across different groups.

In the context of a mixed ANOVA, the repeated measures involve observations taken on the same subjects under different conditions (e.g., measurements taken at different time points), while the independent groups allow the researcher to analyze how these measures may differ across distinct groups (e.g., a treatment group versus a control group). This comprehensive approach is vital in psychological research where understanding both individual variability and group differences is important.

Other statistical methods mentioned are not suitable for this specific scenario. Single-factor ANOVA only examines one independent variable's effect across groups but does not account for repeated measures on the same subjects. The Mann-Whitney U test is a non-parametric test used for comparing differences between two independent groups but does not apply to situations involving repeated measures. The paired t-test is ideal for comparing two related groups but is limited to situations where there are only two related samples,