1. The magnitude of the variance due to individual differences must be measured and subtracted out of the variance between treatments in the numerator of the F-ratio for a repeated-measures ANOVA. 2. A basic assumption required of repeated-measures ANOVA is that all observations are independent of one another. 3. A basic assumption required of repeated-measures ANOVA is the assumption of homogeneity of variances across experimental conditions. 4. A basic assumption required of repeated-measures ANOVA is that the population distribution of individual scores within each treatment is distributed normally (or at least that the population distribution of sampling means within each treatment is distributed normally). 5. A basic assumption required of repeated-measures ANOVA is the assumption of compound symmetry, which states that not only the variances are homogenous across experimental conditions but that the covariances between scores are homogenous across experimental conditions 6. A repeated-measures study uses a sample of n = 10 participants to evaluate the mean differences among three treatment conditions. The analysis of variance for this study will have dftotal = 9. 7. After completing a repeated-measures ANOVA with at least three time points, you should always use the pooled error term when comparing two specific means. 8. For a repeated-measures research study comparing 2 treatment conditions with a sample of n = 8 subjects, the F-ratio would have df = 1, 7. Solution am trying to learn repeated-measures ANOVA (analysis of variance) in statistics for psychology. And I have questions that I need to answer for the class, but I need a greater understanding before I can do the computations. So any help you can provide for me will be greatly appreciated. Things that I am not too sure about (the wording in the textbook is to superfluous, and confusing me): 1) Will a repeated-measures ANOVA (relative to a one-way ANOVA) be more likely to increase power to reject the null hypothesis when systematic differences between subjects emerge across time? OR Will a repeated-measures ANOVA be more likely to increase power when systematic differences between subjects do not emerge across time? 2) Is it wrong to say that the error term in a repeated-measures ANOVA can be considered an interaction? I am pretty sure this is right but not sure how to explain why. 3) What is partial about r^2 partial? And why does it even make sense to compute effect size in this way for repeated-measures ANOVA? 4) If I find that, in a repeated-measures ANOVA, there was evidence that the assumption of compound symmetry was not met. What should I do? Things that I gathered from reading the textbook and lecture slides: once again the wording for this class is not very clear and way too confusing - I am sure to get some of these wrong, so please correct me if I am. So basically true or false, I will try to figure out what is wrong with it - if you are willing to tell me, that would.