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

Question 1

11-14 A 2 × 2 between-subjects factorial design with ten scores per cell requires participants.

A)10
B)20
C)40
D)80

Answer 1

C

Question 2

11-19 A main effect mean for factor A in a factorial design is represented by.

Answer 2

A

Question 3

In a factorial design, a cell is another term for a(n).

A)individual subject
B)main effect
C)treatment condition
D)level of an independent variable

Answer 3

C

Question 4

The simplest factorial design is a design.

A)1 × 1
B)1 × 2
C)2 × 1
D)2 × 2

Question 5

11-18 In a 2 × 2 factorial design, there will be a total of cell mean(s) for the analysis of variance.

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

Question 6

11-20 A main effect mean in a factorial design represents the.

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 7

11-17 A cell mean in a factorial design is represented by.

Question 8

The one-factor analysis of variance is limited to analyzing research designs using.

A)one level of one independent variable
B)three or more independent variables
C)one independent variable
D)two levels of one independent variable

Question 9

Factorial designs are research designs in which independent variable(s) is/are simultaneously varied.

A)one
B)one or more
C)two or more
D)at least three

Question 10

11-13 A 3 × 2 factorial design creates treatment conditions.

A)six
B)five
C)two
D)three

Question 11

11-11 A 2 × 2 factorial design creates treatment conditions.

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

Question 12

A factorial design is one in which two or more are simultaneously varied.

A)dependent variables
B)levels of one independent variable
C)independent variables
D)extraneous variables

Question 13

11-10 In a factorial design a treatment condition represents a.

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

Question 14

11-16 A 3 × 3 between-subjects factorial design with ten scores per cell requires participants.

A)90
B)30
C)60
D)10

Question 15

A 3 × 3 design has independent variables with levels for each.

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

Question 16

A 2 × 3 factorial design has.

A)two independent variables, each with three levels
B)two independent variables, one with two levels and one with three levels
C)six independent variables
D)three independent variables, each with two levels

Question 17

A limitation of the one-factor analysis of variance is that it can only be used to analyze research designs with.

A)one independent variable
B)two independent variables
C)two levels of one independent variable
D)three or more levels of one independent variable

Question 18

11-12 A 3 × 3 factorial design creates treatment conditions.

A)two
B)six
C)three
D)nine

Question 19

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

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

Question 20

11-15 A 3 × 2 between-subjects factorial design with ten scores per cell requires participants.

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

Question 21

11-42 The difference is involved in the computation of SS in a two-factor between-subjects analysis of variance.

A)Total
B)A
C)Error
D)A × B

Question 22

11-32 represents the effect of factor B and sampling error in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

Question 23

11-40 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.

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

Question 24

11-41 The difference is involved in the computation of SS in a two-factor between-subjects analysis of variance.

A)Total
B)A
C)Error
D)A × B

Question 25

11-28 In a two-factor between-subjects analysis of variance, F statistic(s) is/are calculated.

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

Question 26

11-27 A two-factor between-subjects analysis of variance partitions the total variation into between-groups and within-groups sources.

A)three; one
B)three; no
C)four; no
D)one; three

Question 27

11-34 represents within-cells error variation in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

Question 28

11-26 An interaction is analyzed by comparing differences.

A)between main effect means
B)between a main effect mean and the grand mean
C)among cell means
D)between cell means and the grand mean

Question 29

11-30 represents the total variation in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

Question 30

11-25 An interaction in a factorial design is defined as.

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 31

11-35 The difference . in a two-factor between-subjects analysis of variance reflects the

A)main effect of factor A alone
B)main effect of factor A plus sampling error
C)total variation in the score
D)within-cells error variation

Question 32

11-37 The difference . in a two-factor between-subjects analysis of variance reflects the

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 33

11-38 The term in a two-factor between-subjects analysis of variance reflects the.

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 34

11-24 A main effect of an independent variable in a factorial design is defined as the.

A)effect of one independent variable subtracted from the grand mean
B)difference between the main effect means for factor A and those for factor B
C)sum of main effect means for factors A and B
D)effect of one independent variable averaged across levels of the other independent variable

Question 35

11-31 represents the effect of factor A and sampling error in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

Question 36

11-36 The difference . in a two-factor between-subjects analysis of variance reflects the

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 37

11-33 represents the effect of the interaction of factors A and B and sampling error in the partitioned score of a subject in a two-factor between-subjects analysis of variance.

Question 38

11-43 The difference is involved in the computation of SS in a two-factor between-subjects analysis of variance.

A)Total
B)A
C)B
D)A × B

Question 39

11-21 The effect of one independent variable in a factorial design is called a(n) effect.

A)source
B)level
C)main
D)independent

Question 40

11-39 The difference . in a two-factor between-subjects analysis of variance reflects the

A)main effect of factor A plus sampling error
B)main effect of factor B plus sampling error
C)total variation in the score
D)within-cells error variation

Question 41

11-63 The F statistic for factor B in a two-factor between-subjects analysis of variance is formed by dividing MSB by.

A)MSError
B)MSA
C)MSA × B
D)SSTotal

Question 42

11-44 The term is involved in the computation of SS in a two-factor between-subjects analysis of variance.

A)A × B
B)B
C)Total
D)A

Question 43

11-45 The difference -is involved in the computation of SS in a two-factor between-subjects analysis of variance.

A)Total
B)A
C)B
D)Error

Question 44

11-61

A)6.00; 10.00
B)10.00; 80.00
C)5.00; 12.00
D)10.00; 6.00

Question 45

11-49 The degrees of freedom for factor B in a two-factor between-subjects analysis of variance are given by.

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

Question 46

11-52 Suppose a 2 × 2 between-subjects design had 11 participants randomly assigned to each cell. The df for SSTotal are equal to and the df for SSError are equal to for the analysis of variance of this design.

A)43; 40
B)40; 43
C)44; 43
D)45; 44

Question 47

11-55 Suppose a 3 × 2 between-subjects design had 10 participants randomly assigned to each cell. The df for SSA × B are for the analysis of variance of this design.

A)1
B)2
C)3
D)5

Question 48

11-48 The degrees of freedom for factor A in a two-factor between-subjects analysis of variance are given by.

A)N - 1
B)(a - 1)(b - 1)
C)a - 1
D)a - 2

Question 49

11-50 The degrees of freedom for the interaction of factors A and B in a two-factor between-subjects analysis of variance are given by.

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

Question 50

11-58 The mean square for the interaction of factors A and B in a two-factor between subjects analysis of variance is defined as SSA × B divided by.

A)ab
B)dfA
C)(a - 1)(b - 1)
D)ab - 1

Question 51

11-53 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)59; 1
B)59; 2
C)54; 2
D)60; 2

Question 52

11-54 Suppose a 3 × 2 between-subjects design had 10 participants randomly assigned to each cell. The df for SSB are equal to and the df for SSError are equal to for the analysis of variance of this design.

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

Question 53

11-46 If SSTotal = 500.00, SSA = 150.00, SSB = 50.00, and SSError = 100.00 in a two-factor between-subjects analysis of variance, then SSA × B =.

A)800.00
B)300.00
C)150.00 d 200.00

Question 54

11-51 The degrees of freedom for the error in a two-factor between-subjects analysis of variance are given by.

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

Question 55

11-60

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

Question 56

11-57 The mean square for factor B in a two-factor between-subjects analysis of variance is defined as SSB divided by.

A)b
B)dfTotal
C)SSTotal
D)b - 1

Question 57

11-47 The total degrees of freedom in a two-factor between-subjects analysis of variance is defined as the number of minus one.

A)independent variables
B)treatment conditions
C)scores
D)subjects in each treatment condition

Question 58

11-62 The F statistic for factor A in a two-factor between-subjects analysis of variance is formed by dividing MSA by.

A)MSB
B)MSA × B
C)MSError
D)SSTotal

Question 59

11-56 The mean square for factor A in a two-factor between-subjects analysis of variance is defined as SSA divided by.

A)a
B)a - 1
C)dfTotal
D)SSTotal

Question 60

11-59 The mean square for the error term in a two-factor between-subjects analysis of variance is defined as the SSError divided by.

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

Question 61

11-89 Which of the following is not true if Fobs for the interaction in a two-factor between-subjects analysis of variance is statistically significant?

A)The simple effects of factor A are not equal to the main effect of factor A.
B)The simple effects of factor B are not equal to the main effect of factor B.
C)The simple effect of factor A at B1 is not equal to the simple effect of factor A at B2.
D)The simple effects of factors A and B do not differ from their main effects.

Question 62

11-74 The null hypothesis for the interaction of factors A and B in a 3 × 4 between-subjects analysis of variance is H0:.

Question 63

11-69 If factor A produces a main effect in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

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

Question 64

11-71 If the independent variables interact in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

A)MSA
B)MSB
C)MSA × B
D)all the above

Question 65

11-70 If factor B produces a main effect in a two-factor between-subjects analysis of variance, then will increase in value relative to MSError.

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

Question 66

11-78 H0 is rejected if Fobs in a two-factor between-subjects analysis of variance is its corresponding critical value.

A)smaller than
B)equal to or larger than
C)either smaller than or larger than
D)none of the above

Question 67

11-81 The following values of Fobs occurred in a two-factor between-subjects analysis of variance: F(1, 28) for factor A = 3.47, F(1, 28) for factor B = 4.29, and F(1, 28) for the interaction of factors A and B = 4.10. Fcrit(1, 28) = 4.20 for alpha = .05. In this experiment you would H0 for factor A, H0 for factor B, and H0 for the interaction of factors A and B.

A)reject; reject; reject
B)fail to reject; fail to reject; fail to reject
C)fail to reject; reject; fail to reject
D)reject; fail to reject; reject

Question 68

11-64 The F statistic for interaction in a two-factor between-subjects analysis of variance is formed by dividing MSError into.

A)MSA
B)MSA × B
C)MSB
D)SSTotal

Question 69

11-67 The MSB term in a two-factor between-subjects analysis of variance responds to the systematic variation due to factor B and.

A)sampling error
B)factor A
C)sample size of each treatment group
D)the interaction of factors A and B

Question 70

11-65 If MSA = 4.00, MSB = 10.00, MSA × B = 6.00, and MSError = 2.00 in a two-factor between-subjects analysis of variance, then Fobs for factor A = , Fobs for factor B = ,
And Fobs for the interaction of factors A and B =.

A)3.00; 2.00; 5.00
B)2.00; 5.00; 3.00
C)2.00; 3.00; 5.00
D)5.00; 3.00; 2.00

Question 71

11-84 A effect of an independent variable in a factorial design is the effect of that independent variable at of the other independent variable.

A)simple; both levels
B)simple; only one level
C)factorial; all
D)main; only one level

Question 72

11-79 H0 is not rejected if Fobs in a two-factor between-subjects analysis of variance is its corresponding critical value.

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

Question 73

11-77 The alternative hypothesis for the interaction of factors A and B in a 3 × 4 between-subjects analysis of variance is H1:.

Question 74

11-88 If an interaction of the independent variables occurs in a two-factor between-subjects analysis of variance, then the simple effects of a factor will be to each other and to the main effect for that factor.

A)unequal; unequal
B)unequal; equal
C)equal; equal
D)equal; unequal

Question 75

11-83 A two-factor between-subjects analysis of variance is based upon the assumption that the in the populations sampled.

A)scores are symmetrical and bimodal
B)means are equal
C)means are normally distributed
D)variances of scores are equal

Question 76

11-85 A simple effect refers to the in a factorial design.

A)difference between the main effect means for an independent variable
B)difference between a cell mean and a grand mean
C)difference between a cell mean and a main effect mean
D)effect of one independent variable at only one level of another independent variable

Question 77

11-66 The MSA term in a two-factor between-subjects analysis of variance responds to the systematic variation due to factor A and.

A)the grand mean
B)sampling error
C)sample size of each treatment group
D)degrees of freedom for factor A

Question 78

11-68 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)the grand mean
B)sample size of each treatment group
C)the main effects of factors A and B
D)sampling error

Question 79

11-80 The following values of Fobs occurred in a two-factor between-subjects analysis of variance: F(1, 40) for factor A = 4.76, F(1, 40) for factor B = 3.81, and F(1, 40) for the interaction of factors A and B = 5.03. Fcrit(1, 40) = 4.08 for alpha = .05. In this experiment you would H0 for factor A, H0 for factor B, and H0 for the interaction of factors A and B.

A)reject; reject; reject
B)fail to reject; fail to reject; fail to reject
C)fail to reject; reject; fail to reject
D)reject; fail to reject; reject

Question 80

11-82 A two-factor between-subjects analysis of variance is based upon the assumption that the in the populations sampled are.

A)scores; symmetrical and unimodal
B)scores; normally distributed
C)means; equal
D)means; normally distributed