Brooklyn College, CUNY STAT MISC TRUE/FALSE QUESTIONS CHAPTER 6 1)Multivariate analysis of variance (MANOVA) is designed to test the significance of group differences with several dependent variables

Question

Brooklyn College, CUNY

STAT MISC

TRUE/FALSE QUESTIONS

CHAPTER 6

1)Multivariate analysis of variance (MANOVA) is designed to test the significance of group differences with several dependent variables. -

2. At a minimum, the DVs should have some degree of linearity and share a common conceptual meaning. -

3 . Using more than one DV when comparing treatments or groups based on differing characteristics is good because any worthwhile treatment or substantial characteristic will always affect participants in more than one way. [likely, not always]] -

4 . MANOVA tests whether mean differences among k groups on a combination of DVs are unlikely to have occurred by chance. [likely] -

5. The new DV formed in MANOVA is, in fact, a nonlinear combination of the original measured DVs, combined in such a way as to maximize the group differences. [linear] -

6. The new DV formed in MANOVA is created by developing a linear equation where each measured DV has an associated weight and, when combined and summed, creates maximum separation of group means with respect to the new DV. -

7. A factorial MANOVA is a design that involves multiple IVs as well as multiple DVs. -

8. One advantage of using MANOVA, as opposed to doing a couple of ANOVAs, is the slight improvement in the chances of discovering what actually changes as a result of the differing treatments or characteristics. [improve immensely] -

9. A second advantage of using MANOVA is that it consistently reveals differences not shown in separate ANOVAs. [under certain conditions MANOVA may reveal] -

10. A third advantage of using MANOVA is that the overall Type I error rate is increased. [error I increased if many ANOVAs used instead of one MANOVA] -

11. MANOVA incorporates the interconnections of DVs into the analysis. -

12. The results of MANOVA are sometimes ambiguous with respect to the effects of the IVs on individual DVs. -

13. The calculations for MANOVA are based on scalar algebra. [matrix algebra] -

14. The most commonly used test statistic for MANOVA is Roy’s Largest Root. [Wilks' Lambda] -

15. Wilks’ Lambda (?) is an inverse criterion, which means that the smaller the value of ?, the less evidence for treatment effects or group differences. [the more evidence] -

16. In conducting a MANOVA, one first tests the overall multivariate hypothesis. -

17. If the null hypothesis is retained, it is common practice to stop the interpretation of the analysis at this point and conclude that the treatments or conditions have no effect on the DVs. -

18. One of the assumptions of MANOVA is that the observations within each sample must be randomly sampled and must be dependent on each other. [independent] -

19. A second MANOVA assumption is that the observations on at least one DV must follow a multivariate normal distribution in the group. [on all DVs, and in each group] -

20. A third MANOVA assumption is that the relationships among all pairs of DVs for each cell in the data matrix must be normal. [fourth assumption; must be linear] -

21. Multivariate Analysis of Covariance (MANCOVA) is essentially a combination of MANOVA and ANCOVA. -

22. MANCOVA asks if there are statistically significant mean differences among groups after adjusting the newly created DV for differences on one or more covariates. -

23. In MANCOVA, the effects of the covariates are added to the analysis, leaving the researcher with a clearer picture of the effects of the IVs on the multiple DVs. [removed from] -

24. The null hypothesis being tested in MANCOVA is that the adjusted population mean vectors are not equal. [are equal] -

25. An assumption in MANCOVA is that linear relationships need not exist between all pairs of DVs, all pairs of covariates, and all DV-covariates in each cell. [must exist] -