Within-Subjects Design means participants receive all levels of the independent variable.

Within-Subjects Design means participants receive all levels of the independent variable.

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

Answer: (B)

Within-subjects design refers to a setup where every participant experiences every level of the independent variable. This means each person is measured under all conditions, so they effectively serve as their own control. This reduces variability due to differences between participants and often increases statistical power. For example, if you’re comparing two study techniques, each participant would try both techniques, with the order possibly counterbalanced to avoid order effects. This is different from a between-subjects design, where different participants are assigned to each level of the independent variable. A crossover design is a specific kind of within-subjects approach where treatments are given in sequence to the same participants (often with a washout period). A factorial design involves examining more than one independent variable at once and their interactions, rather than the basic idea of who experiences the levels.

Within-subjects design refers to a setup where every participant experiences every level of the independent variable. This means each person is measured under all conditions, so they effectively serve as their own control. This reduces variability due to differences between participants and often increases statistical power. For example, if you’re comparing two study techniques, each participant would try both techniques, with the order possibly counterbalanced to avoid order effects. This is different from a between-subjects design, where different participants are assigned to each level of the independent variable. A crossover design is a specific kind of within-subjects approach where treatments are given in sequence to the same participants (often with a washout period). A factorial design involves examining more than one independent variable at once and their interactions, rather than the basic idea of who experiences the levels.