Which design is characterized by the same participants being measured across all levels of the independent variable?

Which design is characterized by the same participants being measured across all levels of the independent variable?

• A. Between-Subjects Design
• B. Cross-Sectional Design
• C. Factorial Design
• D. Within-Subjects Design

Answer: (D)

The key idea here is repeated measures within a single group of participants. In this design, every participant experiences every level of the independent variable, so each person serves as their own control. This setup makes it easier to detect how the IV levels influence the outcome because individual differences that often add noise to the data are held constant across conditions. Because the same participants are measured under all conditions, there’s less variability from one participant to another, which typically increases statistical power and reduces the number of participants needed. But this approach can bring carryover or order effects—how one condition influences responses in another. To handle that, researchers often counterbalance the order of conditions or insert rest periods. How this differs from other common designs helps clarify the distinction. A design that uses different participants for each level of the IV relies on between-subjects comparisons, so individual differences across groups can confound effects and require more participants to achieve the same power. A cross-sectional approach typically involves observing different groups at a single point in time rather than repeatedly measuring the same individuals across levels. A factorial design refers to studying more than one IV and their interactions, and it can be implemented as within-subjects or between-subjects, but the defining feature in this question is the repeated measurement of the same participants across all levels of a single IV.

The key idea here is repeated measures within a single group of participants. In this design, every participant experiences every level of the independent variable, so each person serves as their own control. This setup makes it easier to detect how the IV levels influence the outcome because individual differences that often add noise to the data are held constant across conditions.

Because the same participants are measured under all conditions, there’s less variability from one participant to another, which typically increases statistical power and reduces the number of participants needed. But this approach can bring carryover or order effects—how one condition influences responses in another. To handle that, researchers often counterbalance the order of conditions or insert rest periods.

How this differs from other common designs helps clarify the distinction. A design that uses different participants for each level of the IV relies on between-subjects comparisons, so individual differences across groups can confound effects and require more participants to achieve the same power. A cross-sectional approach typically involves observing different groups at a single point in time rather than repeatedly measuring the same individuals across levels. A factorial design refers to studying more than one IV and their interactions, and it can be implemented as within-subjects or between-subjects, but the defining feature in this question is the repeated measurement of the same participants across all levels of a single IV.