Question 1

The calculations for MANOVA are based on scalar algebra.

Accepted Answer

A) True 
 B)False  False

Question 2

A second advantage of using MANOVA is that it consistently reveals differences not shown in separate ANOVAs.

Accepted Answer

A) True 
 B)False  False

Question 3

If Box's test is not significant when interpreting MANCOVA, which of the following test statistics should be used when interpreting the homogeneity of regression slopes and the subsequent multivariate tests?

Accepted Answer

A)  Wilks' Lambda. 
B)  Pillai's Trace. 
C)  Hotelling's Trace. 
D)  All of the above. 
A)  Wilks' Lambda. 
B)  Pillai's Trace. 
C)  Hotelling's Trace. 
D)  All of the above. A

Question 4

A violation of the assumption of homogeneity of regression slopes (as well as regression planes and hyperplanes) in MANCOVA is an indication that:

Accepted Answer

A)  There is a covariate by treatment (IV) interaction. 
B)  There is a covariate by DV interaction. 
C)  Neither (a) nor (b) is correct. 
D)  Both (a) and (b) are correct. 
A)  There is a covariate by treatment (IV) interaction. 
B)  There is a covariate by DV interaction. 
C)  Neither (a) nor (b) is correct. 
D)  Both (a) and (b) are correct.

Question 5

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

Accepted Answer

A) True 
 B)False

Question 6

In summary, the first step in interpreting the MANCOVA results is to evaluate the preliminary MANCOVA results that include:

Accepted Answer

A)  Box's test. 
B)  Test for homogeneity of regression slopes. 
C)  Box's test and the test for homogeneity of regression slopes. 
D)  F ratio. 
A)  Box's test. 
B)  Test for homogeneity of regression slopes. 
C)  Box's test and the test for homogeneity of regression slopes. 
D)  F ratio.

Question 7

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.

Accepted Answer

A) True 
 B)False

Question 8

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

Accepted Answer

A) True 
 B)False

Question 9

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.

Accepted Answer

A) True 
 B)False

Question 10

Examining group means (before and after covariate adjustment) for each DV in MANCOVA can assist in determining how groups differed for each DV because:

Accepted Answer

A)  MANCOVA provides post hoc analyses. 
B)  MANCOVA does not provide post hoc analyses. 
C)  MANCOVA provides post hoc analyses when there is only one covariate. 
D)  MANCOVA provides post hoc analyses when there is only one factor. 
A)  MANCOVA provides post hoc analyses. 
B)  MANCOVA does not provide post hoc analyses. 
C)  MANCOVA provides post hoc analyses when there is only one covariate. 
D)  MANCOVA provides post hoc analyses when there is only one factor.

Question 11

Wilks' Lambda (Ʌ) is an inverse criterion, which means that the smaller the value of Ʌ, the less evidence for treatment effects or group differences.

Accepted Answer

A) True 
 B)False

Question 12

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

Accepted Answer

A) True 
 B)False

Question 13

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

Accepted Answer

A) True 
 B)False

Question 14

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

Accepted Answer

A) True 
 B)False

Question 15

Initial assessments of normality with MANCOVA is done through the inspection of:

Accepted Answer

A)  Histograms. 
B)  Boxplots. 
C)  Normal Q-Q plots. 
D)  All of the above. 
A)  Histograms. 
B)  Boxplots. 
C)  Normal Q-Q plots. 
D)  All of the above.

Question 16

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

Accepted Answer

A) True 
 B)False

Question 17

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

Accepted Answer

A) True 
 B)False

Question 18

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.

Accepted Answer

A) True 
 B)False

Question 19

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

Accepted Answer

A) True 
 B)False

Question 20

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.

Accepted Answer

A) True 
 B)False

The calculations for MANOVA are based on scalar algebra.

A second advantage of using MANOVA is that it consistently reveals differences not shown in separate ANOVAs.

If Box's test is not significant when interpreting MANCOVA, which of the following test statistics should be used when interpreting the homogeneity of regression slopes and the subsequent multivariate tests?

A violation of the assumption of homogeneity of regression slopes (as well as regression planes and hyperplanes) in MANCOVA is an indication that:

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

In summary, the first step in interpreting the MANCOVA results is to evaluate the preliminary MANCOVA results that include:

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.

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

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.

Examining group means (before and after covariate adjustment) for each DV in MANCOVA can assist in determining how groups differed for each DV because:

Wilks' Lambda (Ʌ) is an inverse criterion, which means that the smaller the value of Ʌ, the less evidence for treatment effects or group differences.

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

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

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

Initial assessments of normality with MANCOVA is done through the inspection of:

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

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

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.

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

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.

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Analysis of Covariance

Multiple Regression

Path Analysis

Factor Analysis

Discriminant Analysis

Logistic Regression

Filters

Exam 6: Multivariate Analysis of Variance and Covariance

The calculations for MANOVA are based on scalar algebra.

A second advantage of using MANOVA is that it consistently reveals differences not shown in separate ANOVAs.

If Box's test is not significant when interpreting MANCOVA, which of the following test statistics should be used when interpreting the homogeneity of regression slopes and the subsequent multivariate tests?

A violation of the assumption of homogeneity of regression slopes (as well as regression planes and hyperplanes) in MANCOVA is an indication that:

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

In summary, the first step in interpreting the MANCOVA results is to evaluate the preliminary MANCOVA results that include:

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.

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

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.

Examining group means (before and after covariate adjustment) for each DV in MANCOVA can assist in determining how groups differed for each DV because:

Wilks' Lambda (Ʌ) is an inverse criterion, which means that the smaller the value of Ʌ, the less evidence for treatment effects or group differences.

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

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

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

Initial assessments of normality with MANCOVA is done through the inspection of:

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

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

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.

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

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.

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Analysis of Covariance

Multiple Regression

Path Analysis

Factor Analysis

Discriminant Analysis

Logistic Regression

Filters