Question 1

If factor-covariate interaction is not significant, then one can proceed with interpreting the Levene's test as well as proceeding with the full ANCOVA.

Accepted Answer

A) True 
 B)False  True

Question 2

The second major purpose for the use of ANCOVA involves a statistical adjustment procedure where the means of the DVs for each group are adjusted to where they would be if all groups had scored equally on the covariate.

Accepted Answer

A) True 
 B)False  True

Question 3

The first step in interpreting the results of ANCOVA is to:

Accepted Answer

A)  Determine if factor interaction is present by examining the F ratio and p value for interaction. 
B)  Obtain a measure of effect size. 
C)  Test the various combinations of levels for Factor A and Factor 
D)  Only (a) and (b) are correct. 
A)  Determine if factor interaction is present by examining the F ratio and p value for interaction. 
B)  Obtain a measure of effect size. 
C)  Test the various combinations of levels for Factor A and Factor 
D)  Only (a) and (b) are correct. A

Question 4

Analysis of covariance (ANCOVA) mirrors the ordinary analysis of variance (ANOVA), but only before the effect of the covariate has been partialed out.

Accepted Answer

A) True 
 B)False

Question 5

A two-way ANCOVA tests the following three separate hypotheses simultaneously in one analysis:

Accepted Answer

A)  Two of the hypotheses test the significance of the levels of the two IVs separately, after removing the effects of the covariate. 
B)  The third tests the significance of the interaction of the levels of the two IVs, also after removing the effects of the covariate. 
C)  The third tests the significance of the interaction of the levels of the IVs and DV after removing the effects of the covariate. 
D)  (a) and (b) are correct. 
A)  Two of the hypotheses test the significance of the levels of the two IVs separately, after removing the effects of the covariate. 
B)  The third tests the significance of the interaction of the levels of the two IVs, also after removing the effects of the covariate. 
C)  The third tests the significance of the interaction of the levels of the IVs and DV after removing the effects of the covariate. 
D)  (a) and (b) are correct.

Question 6

If the Levene's test is significant, then homogeneity of variance is assumed.

Accepted Answer

A) True 
 B)False

Question 7

ANCOVA adjusts group means as if participants scored equally on the covariates.

Accepted Answer

A) True 
 B)False

Question 8

F ratios and p values for each covariate indicate the degree to which the covariate significantly influences the DV.

Accepted Answer

A) True 
 B)False

Question 9

The third major purpose for the use of ANCOVA is to interpret differences in levels of the IV when several DVs are included in the analysis.

Accepted Answer

A) True 
 B)False

Question 10

A violation of the assumption of homogeneous regression slopes is crucial with respect to the validity of the results of ANCOVA.

Accepted Answer

A) True 
 B)False

Question 11

If the F test of factor-covariate interaction is significant, then the full ANCOVA should not be conducted.

Accepted Answer

A) True 
 B)False

Question 12

Interpretation of ANCOVA results is similar to that of ANOVA.

Accepted Answer

A) True 
 B)False

Question 13

Line graphs are typically created to graphically represent any interaction between/among covariates based upon the DV.

Accepted Answer

A) True 
 B)False

Question 14

The effects of the covariate are removed by adjusting the scores on the IV in order to reflect the initial differences on the covariate.

Accepted Answer

A) True 
 B)False

Question 15

When the covariate adjustment is made, a portion of the treatment effect will be removed.

Accepted Answer

A) True 
 B)False

Question 16

Regression is incorporated into ANCOVA in order to predict scores on the DV based on knowledge of scores on the covariate.

Accepted Answer

A) True 
 B)False

Question 17

The logic of ANCOVA is identical to the logic behind ANOVA.

Accepted Answer

A) True 
 B)False

Question 18

One of the assumptions for ANCOVA is that the observations within each sample must be randomly sampled and must be dependent on one another.

Accepted Answer

A) True 
 B)False

Question 19

If there exists a weak correlation between two covariates, they will remove relatively unique portions of the error variance from the DV, which is:

Accepted Answer

A)  Advantageous. 
B)  Disadvantageous. 
C)  Of no consequence because the correlation is weak. 
D)  None of the above is correct. 
A)  Advantageous. 
B)  Disadvantageous. 
C)  Of no consequence because the correlation is weak. 
D)  None of the above is correct.

Question 20

The initial phase of the analysis with ANCOVA involves the statistical adjustments of the DV group means in order to control for the effects of the covariate on the IVs.

Accepted Answer

A) True 
 B)False

If factor-covariate interaction is not significant, then one can proceed with interpreting the Levene's test as well as proceeding with the full ANCOVA.

The second major purpose for the use of ANCOVA involves a statistical adjustment procedure where the means of the DVs for each group are adjusted to where they would be if all groups had scored equally on the covariate.

The first step in interpreting the results of ANCOVA is to:

Analysis of covariance (ANCOVA) mirrors the ordinary analysis of variance (ANOVA), but only before the effect of the covariate has been partialed out.

A two-way ANCOVA tests the following three separate hypotheses simultaneously in one analysis:

If the Levene's test is significant, then homogeneity of variance is assumed.

ANCOVA adjusts group means as if participants scored equally on the covariates.

F ratios and p values for each covariate indicate the degree to which the covariate significantly influences the DV.

The third major purpose for the use of ANCOVA is to interpret differences in levels of the IV when several DVs are included in the analysis.

A violation of the assumption of homogeneous regression slopes is crucial with respect to the validity of the results of ANCOVA.

If the F test of factor-covariate interaction is significant, then the full ANCOVA should not be conducted.

Interpretation of ANCOVA results is similar to that of ANOVA.

Line graphs are typically created to graphically represent any interaction between/among covariates based upon the DV.

The effects of the covariate are removed by adjusting the scores on the IV in order to reflect the initial differences on the covariate.

When the covariate adjustment is made, a portion of the treatment effect will be removed.

Regression is incorporated into ANCOVA in order to predict scores on the DV based on knowledge of scores on the covariate.

The logic of ANCOVA is identical to the logic behind ANOVA.

One of the assumptions for ANCOVA is that the observations within each sample must be randomly sampled and must be dependent on one another.

If there exists a weak correlation between two covariates, they will remove relatively unique portions of the error variance from the DV, which is:

The initial phase of the analysis with ANCOVA involves the statistical adjustments of the DV group means in order to control for the effects of the covariate on the IVs.

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Multivariate Analysis of Variance and Covariance

Multiple Regression

Path Analysis

Factor Analysis

Discriminant Analysis

Logistic Regression

Filters

Exam 5: Analysis of Covariance

If factor-covariate interaction is not significant, then one can proceed with interpreting the Levene's test as well as proceeding with the full ANCOVA.

The second major purpose for the use of ANCOVA involves a statistical adjustment procedure where the means of the DVs for each group are adjusted to where they would be if all groups had scored equally on the covariate.

The first step in interpreting the results of ANCOVA is to:

Analysis of covariance (ANCOVA) mirrors the ordinary analysis of variance (ANOVA), but only before the effect of the covariate has been partialed out.

A two-way ANCOVA tests the following three separate hypotheses simultaneously in one analysis:

If the Levene's test is significant, then homogeneity of variance is assumed.

ANCOVA adjusts group means as if participants scored equally on the covariates.

F ratios and p values for each covariate indicate the degree to which the covariate significantly influences the DV.

The third major purpose for the use of ANCOVA is to interpret differences in levels of the IV when several DVs are included in the analysis.

A violation of the assumption of homogeneous regression slopes is crucial with respect to the validity of the results of ANCOVA.

If the F test of factor-covariate interaction is significant, then the full ANCOVA should not be conducted.

Interpretation of ANCOVA results is similar to that of ANOVA.

Line graphs are typically created to graphically represent any interaction between/among covariates based upon the DV.

The effects of the covariate are removed by adjusting the scores on the IV in order to reflect the initial differences on the covariate.

When the covariate adjustment is made, a portion of the treatment effect will be removed.

Regression is incorporated into ANCOVA in order to predict scores on the DV based on knowledge of scores on the covariate.

The logic of ANCOVA is identical to the logic behind ANOVA.

One of the assumptions for ANCOVA is that the observations within each sample must be randomly sampled and must be dependent on one another.

If there exists a weak correlation between two covariates, they will remove relatively unique portions of the error variance from the DV, which is:

The initial phase of the analysis with ANCOVA involves the statistical adjustments of the DV group means in order to control for the effects of the covariate on the IVs.

Introduction to Multivariate Statistics

A Guide to Multivariate Techniques

Pre-Analysis Data Screening

Factorial Analysis of Variance

Multivariate Analysis of Variance and Covariance

Multiple Regression

Path Analysis

Factor Analysis

Discriminant Analysis

Logistic Regression

Filters