15.9: Power of Within-Subjects Designs Demo Last updated Save as PDF Share Share Share Tweet Contributed by David LaneAssociate Professor (Psychology, Statistics, and Management) at Rice University Chapter: Front1. Introduction2. Graphing Distributions3. Summarizing Distributions4. Describing Bivariate Data5. Probability6. Research Design7. Normal Distribution8. Advanced Graphs9. Sampling Distributions10. Estimation11. Logic of Hypothesis Testing12. Tests of Means13. Power14. Regression15. Analysis of Variance16. Transformations17. Chi Square18. Distribution Free Tests19. Effect Size20. Case Studies21. Calculators22. Glossary Section: ContentsIntroductionANOVA DesignsOne-Factor ANOVAOne-Way DemoMulti-Factor Between-SubjectsUnequal nTests SupplementingWithin-SubjectsPower of Within-Subjects Designs DemoStatistical LiteracyExercises Home | Previous Section | Next Section Power of Within-Subjects Designs Demonstration Learning Objectives State the relationship between the correlation and power. Stae the relationship between variance and power. State the relationship between the difference in population means and power. State the effect of using a one-tailed test on power. Instructions This simulation demonstrates the effect of the correlation between measures in a one-way within-subjects ANOVA with two levels. This test is equivalent to a correlated t test. The default values for this demonstration are for an experiment with 10 subjects each measured under two conditions. The population difference for the two conditions is 1.85 and the variance in each of the conditions is 4.0. The graph shows the power of the test as a function ofthe population correlation between the two scores for the 0.10, 0.05, and 0.01 significance levels. The power of an independent-groups t test (which assumes the correlation is 0) is shown by the x's. Experiment with different combinations of the parameters. Is the correlation an important factor in power? Illustrated Instructions Video Demo The video begins by changing the population variance to 8 and increases the sample size to 25 and then reduces it to 5. Notice the impact that these changes have on the relationship between power and population correlation. The video concludes by changing the mean difference to 4. Video Demo