Question 1

H0 is not rejected if Fobs in a two-factor between-subjects analysis of variance is its corresponding critical value.

Accepted Answer

A) less than 
B) equal to 
C) larger than 
D) none of the above

Question 2

The difference  $\bar { X } _ { A } - \bar { X } _ { G }$ is involved in the computation of SS in a two-factor between-subjects analysis of variance.

Accepted Answer

A) Total 
B) A 
C) Error 
D) A × B

Question 3

If the independent variables interact in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

Accepted Answer

A) MSA 
B) MSB 
C) MSA × B 
D) all the above

Question 4

In a two-factor between-subjects analysis of variance, the interaction of two independent variables is reflected in the remaining deviation of a mean from a mean after the main effects of each independent variable have been removed.

Accepted Answer

A) main effect; grand 
B) main effect; treatment group 
C) cell; grand 
D) cell; main effect

Question 5

If factor B produces a main effect in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

Accepted Answer

A) MSA 
B) MSA × B 
C) MSB 
D) both MSB and MSA × B

Question 6

The MSA × B term in a two-factor between-subjects analysis of variance responds to the systematic variation due to the interaction of factors A and B and.

Accepted Answer

A) the grand mean 
B) sample size of each treatment group 
C) the main effects of factors A and B 
D) sampling error

Question 7

A main effect mean in a factorial design represents the.

Accepted Answer

A) total of all scores divided by N 
B) principal source of variation in an experiment 
C) mean of all levels of one independent variable 
D) mean of one level of an independent variable

Question 8

The term     $\bar { X } _ { A B } - \bar { X } _ { A } - \bar { X } _ { B } + \bar { X } _ { G }$ in a two-factor between-subjects analysis of variance reflects the.

Accepted Answer

A) main effect of factor A plus sampling error 
B) main effect of factor B plus sampling error 
C) effect of the interaction of factors A and B plus sampling error 
D) within-cells error variation

Question 9

In a factorial design a treatment condition represents a.

Accepted Answer

A) placebo treatment 
B) level of one independent variable 
C) main effect treatment 
D) combination of one level from each independent variable

Question 10

Suppose a 3 × 2 between-subjects design had 10 participants randomly assigned to each cell. The df for SSTotal are equal to and the df for SSA are equal to for the analysis of variance of this design.

Accepted Answer

A) 59; 1 
B) 59; 2 
C) 54; 2 
D) 60; 2

Question 11

A 3 × 2 between-subjects factorial design with ten scores per cell requires participants.

Accepted Answer

A) 50 
B) 60 
C) 10 
D) 30

Question 12

A distinguishing characteristic of a figure of an interaction is that the lines representing the levels of the independent variables are.

Accepted Answer

A) parallel 
B) U-shaped 
C) inverted U-shaped 
D) nonparallel

Question 13

The degrees of freedom for the error in a two-factor between-subjects analysis of variance are given by.

Accepted Answer

A) N - 1 
B) (a - 1)(b - 1) 
C) ab(nAB - 1) 
D) ab(nAB + 1)

Question 14

The F statistic for factor A in a two-factor between-subjects analysis of variance is formed by dividing MSA by.

Accepted Answer

A) MSB 
B) MSA × B 
C) MSError 
D) SSTotal

Question 15

In a 2 × 2 factorial design, there will be a total of cell mean(s) for the analysis of variance.

Accepted Answer

A) four 
B) eight 
C) two 
D) one

Question 16

If $S S _ { \text {Total } } = 150.00 , S S _ { A } = 8.00 , S S _ { B } = 10.00 , S S _ { A } \times B = 12.00 , S S _ { \text {Error } } = 120.00 , d f$ Total $= 65$, $d f _ { A } = 2 , d f _ { B } = 1 , d f _ {{ A } \times B} = 2$, and $d f _ { \text {Error } } = 60$ in a two-factor between-subjects analysis of variance, then $M S _ { A } = \ldots$ and $M S _ { \text {Error } } =$

Accepted Answer

A) 4.00; 6.00 
B) 4.00; 2.00 
C) 16.00; 7200.00 
D) 8.00; 30.00

Question 17

A cell mean in a factorial design is represented by.

Accepted Answer

A)  $\bar { X } _ { A }$ 
B)  $\bar { X } _B$ 
C)  $\bar { X } _ { A B }$ 
D)  $\bar { X } _ { \mathrm { G } }$

Question 18

A 2 × 2 × 2 factorial design indicates independent variables are used with each variable assuming levels.

Accepted Answer

A) three; two 
B) three; three 
C) two; three 
D) two; two

Question 19

An interaction in a factorial design is defined as.

Accepted Answer

A) a situation in which the effect of one independent variable depends upon the level of the other independent variable 
B) the difference between main effect means for factors A and B 
C) the sum of main effect means for factors A and B 
D) the effect of one independent variable averaged across levels of the other independent variable

Question 20

If the results of a two-factor between-subjects analysis of variance were summarized as F(3, 72) = 4.06, p < .05 for factor B, then you would know that the number of levels of factor B was .

Accepted Answer

A) 2 
B) 3 
C) 4 
D) not enough

Exam 11: Two-Factor Between-Subjects Analysis of Variance

H0 is not rejected if Fobs in a two-factor between-subjects analysis of variance is its corresponding critical value.

If the independent variables interact in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

In a two-factor between-subjects analysis of variance, the interaction of two independent variables is reflected in the remaining deviation of a mean from a mean after the main effects of each independent variable have been removed.

If factor B produces a main effect in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

The MSA × B term in a two-factor between-subjects analysis of variance responds to the systematic variation due to the interaction of factors A and B and.

A main effect mean in a factorial design represents the.

In a factorial design a treatment condition represents a.

Suppose a 3 × 2 between-subjects design had 10 participants randomly assigned to each cell. The df for SSTotal are equal to and the df for SSA are equal to for the analysis of variance of this design.

A 3 × 2 between-subjects factorial design with ten scores per cell requires participants.

A distinguishing characteristic of a figure of an interaction is that the lines representing the levels of the independent variables are.

The degrees of freedom for the error in a two-factor between-subjects analysis of variance are given by.

The F statistic for factor A in a two-factor between-subjects analysis of variance is formed by dividing MSA by.

In a 2 × 2 factorial design, there will be a total of cell mean(s) for the analysis of variance.

A cell mean in a factorial design is represented by. $A cell mean in a factorial design is represented by. A) \bar { X } _ { A } B) \bar { X } _B C) \bar { X } _ { A B } D) \bar { X } _ { \mathrm { G } }$

A 2 × 2 × 2 factorial design indicates independent variables are used with each variable assuming levels.

An interaction in a factorial design is defined as.

If the results of a two-factor between-subjects analysis of variance were summarized as F(3, 72) = 4.06, p < .05 for factor B, then you would know that the number of levels of factor B was .

Exam 11: Two-Factor Between-Subjects Analysis of Variance

H0 is not rejected if Fobs in a two-factor between-subjects analysis of variance is its corresponding critical value.

The difference XˉA−XˉG\bar { X } _ { A } - \bar { X } _ { G }XˉA​−XˉG​ is involved in the computation of SS in a two-factor between-subjects analysis of variance.

If the independent variables interact in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

In a two-factor between-subjects analysis of variance, the interaction of two independent variables is reflected in the remaining deviation of a mean from a mean after the main effects of each independent variable have been removed.

If factor B produces a main effect in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

The MSA × B term in a two-factor between-subjects analysis of variance responds to the systematic variation due to the interaction of factors A and B and.

A main effect mean in a factorial design represents the.

The term XˉAB−XˉA−XˉB+XˉG\bar { X } _ { A B } - \bar { X } _ { A } - \bar { X } _ { B } + \bar { X } _ { G }XˉAB​−XˉA​−XˉB​+XˉG​ in a two-factor between-subjects analysis of variance reflects the.

In a factorial design a treatment condition represents a.

Suppose a 3 × 2 between-subjects design had 10 participants randomly assigned to each cell. The df for SSTotal are equal to and the df for SSA are equal to for the analysis of variance of this design.

A 3 × 2 between-subjects factorial design with ten scores per cell requires participants.

A distinguishing characteristic of a figure of an interaction is that the lines representing the levels of the independent variables are.

The degrees of freedom for the error in a two-factor between-subjects analysis of variance are given by.

The F statistic for factor A in a two-factor between-subjects analysis of variance is formed by dividing MSA by.

In a 2 × 2 factorial design, there will be a total of cell mean(s) for the analysis of variance.

A cell mean in a factorial design is represented by.

A 2 × 2 × 2 factorial design indicates independent variables are used with each variable assuming levels.

An interaction in a factorial design is defined as.

If the results of a two-factor between-subjects analysis of variance were summarized as F(3, 72) = 4.06, p < .05 for factor B, then you would know that the number of levels of factor B was .

A cell mean in a factorial design is represented by. $A cell mean in a factorial design is represented by. A) \bar { X } _ { A } B) \bar { X } _B C) \bar { X } _ { A B } D) \bar { X } _ { \mathrm { G } }$