Which design is best to avoid carryover effects while keeping participant numbers reasonable?

Which design is best to avoid carryover effects while keeping participant numbers reasonable?

• A. Independent Groups Design
• B. Repeated Measures Design
• C. Matched Pairs Design
• D. Single-subject Design

Answer: (C)

Carryover effects happen when taking part in one condition influences performance in another condition, which is a risk when the same participants are exposed to multiple conditions. The design that best avoids this while keeping the number of participants manageable is matched pairs. In a matched-pairs setup, participants are paired on relevant characteristics (like baseline ability or other factors that could affect the outcome), and then within each pair, one participant is randomly assigned to each condition. This means no participant experiences more than one condition, so there’s no carryover from one condition to the next. At the same time, matching reduces variability due to individual differences, which can give you more statistical power without needing a large increase in participants. By contrast, an independent groups design also avoids carryover but typically requires more participants to achieve the same power, while a repeated-measures design, though efficient, risks carryover unless complex counterbalancing is used.

Carryover effects happen when taking part in one condition influences performance in another condition, which is a risk when the same participants are exposed to multiple conditions. The design that best avoids this while keeping the number of participants manageable is matched pairs. In a matched-pairs setup, participants are paired on relevant characteristics (like baseline ability or other factors that could affect the outcome), and then within each pair, one participant is randomly assigned to each condition. This means no participant experiences more than one condition, so there’s no carryover from one condition to the next. At the same time, matching reduces variability due to individual differences, which can give you more statistical power without needing a large increase in participants. By contrast, an independent groups design also avoids carryover but typically requires more participants to achieve the same power, while a repeated-measures design, though efficient, risks carryover unless complex counterbalancing is used.