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1. Two-Factor Analysis of Variance
2. The repeated-measures ANOVA is a two-stage process. Which of the following accurately describes what happens during this process?
3. A repeated-measures ANOVA has SS within treatments = 26 and between subjects = 12. For this analysis, what is the value for error?
4. A two-factor study with two levels of factor A and three levels of factor B uses a separate group of n = 5 participants in each treatment condition. How many participants are needed for the entire study?
5. In a repeated-measures ANOVA, the variability within treatments is divided into two components. What are they?
6. A two-factor research study is used to evaluate the effectiveness of a new blood pressure medication. In this two-factor study, Factor A is medication versus no medication and factor B is male versus female. The medicine is expected to reduce blood pressure for both males and females, but it is expected to have a much greater effect on males. What pattern of results should be obtained if the medication works as predicted?
7. The results of a two-factor analysis of variance produce df = 1, 30 for the F-ratio for factor A, and df = 2, 30 for the F-ratio for the AxB interaction. Based on this information, how many levels of factor B were compared in the study?
8. A researcher reports an F-ratio with df = 2, 40 from a repeated-measures ANOVA. How many treatment conditions were compared in this experiment?
9. How many separate groups of participants would be needed for an independent-measures, two-factor study with 3 levels of factor A and 4 levels of factor B?
10. The results from a two-factor analysis of variance show a significant main effect for factor A and a significant main effect for factor B. Based on this information, what can you conclude about the interaction?
11. A two-factor analysis of variance produces an F-ratio for factor A that has df = 3, 36. This analysis is comparing three different levels of factor A.
12. For an independent-measures two-factor analysis of variance, all F-ratios use the same denominator.
13. A repeated-measures study uses a sample of n = 8 participants to evaluate the mean differences between two treatment conditions. The analysis of variance for this study will have an error = 7.
14. Because the repeated-measures ANOVA removes variance caused by individual differences, it usually is more likely to detect a treatment effect than the independent-measures ANOVA. Obtaining a significant interaction means that both factors A and B have significant main effects.
15. A repeated-measures study uses a sample of n = 10 participants to evaluate the mean differences among three treatment conditions. For this study, between subjects = 18.

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