For a repeated-measures study, if other factors are held constant, then an increase in sample size will lead to an decrease in measures of effect size True False
The correct answer and explanation is:
Correct Answer: False
In a repeated-measures study, increasing the sample size does not lead to a decrease in measures of effect size. In fact, effect size is generally independent of sample size. This is a common misunderstanding.
Effect size, such as Cohen’s d or eta-squared (η²), is a standardized value that reflects the magnitude of the difference or relationship being studied. It tells us how strong the effect is, regardless of how many subjects are included in the study.
Effect size is calculated using the difference between means and the standard deviation. In a repeated-measures design, the effect size usually depends on the mean differences between the conditions and the variability within subjects. When you increase the sample size, it improves the statistical power of the test and increases the precision of the estimate, but it does not reduce the size of the effect itself. In fact, with a larger sample, the estimate of the effect size might become more stable and accurate, but the actual value of the effect size remains roughly the same if the underlying effect in the population remains unchanged.
Also, repeated-measures designs are efficient because they reduce variability by comparing each participant to themselves. This often results in larger effect sizes compared to between-subjects designs, not smaller.
To summarize: increasing sample size improves the reliability and power of your study, but it does not lead to a decrease in effect size. So the statement in the question is false.