Question

Participants enter a research study with unique characteristics that produce different scores from one person to another. For an independent-measures study, these individual differences can cause problems. Briefly explain how these problems are eliminated or reduced with a repeated-measures study

Answer 1

Answer:

Consider two datasets which produce a mean difference of Md = 3.5.

As a researcher, you would like to know if this mean difference of 3.5 was caused by the treatment. If this study were to be an independent-measures design, then it would be possible that the participants in treatment had different characteristics (For example, a mean difference of 3.5 in test scores could be because the first group participants were inherently more capable that those in the second group). But with repeated -measures design, this problem is eliminated because the same set of participants is used in the treatment.
Another advantage of repeated-measures design is that since the SS and variance are computed for the difference in scores, the individual differences within samples is eliminated. (For example, a big difference between two data values A and B in both treatments are eliminated once we take the difference). Because the individual differences are eliminated, the variance and S are considerably reduced, and this increases the probability of finding a significant result.