Complex Mixed Design: Two Between-Subjects Factors and One Within-Subjects Factor - Analysis of Variance Designs - page 417

This Table presents the
unadjusted p -value. The
Bonferroni adjustment (not
shown) can be calculated by
multiplying the unadjusted
p -value by 28 (the number
of all pairwise comparisons).

Differences of Least Squares Means

Standard
Error Effect

age ∗ gender ∗ attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age

age gender attract age gender attract Estimate DF t Value Pr > ltl

1
1
1
1
1
1
1
1
1
1
1
1
1

1
1
1
1
1
1
1
1
1
1
1

1
1
1

1
1
1

1
2
2

2 −1.4000 0.3536

0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.5196

16 −3.96 0.0011

1 2.2000

1.2000

16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16

4.23
2.31

0.0006
0.0346 2

1
1

2
2

1
1

1 − 3.0000 − 5.77 < .0001

2 − 2.8000 − 5.39 < .0001

∗ gender ∗ attract

1.92

1
1

2 2

2
2

1 1.0000 0.0723

0.0014

age ∗ gender ∗ attract

− 2.0000 − 3.85

2 2

age ∗ gender ∗ attract

6.93
5.00

< .0001

2
2
2

1
1

1 3.6000

2.6000

age ∗ gender ∗ attract

2 2 0.0001

0.0072
0.0160
0.0003
0.2652
0.0121

age attract

age ∗ gender ∗ attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age

∗ gender ∗

− 1.6000 − 3.08

2
2
2

1
1

1

− 1.4000 − 2.69

2
2
2

2

4.62

1
1

2
2

1 2.4000

2 2 −0.6000

−1.0000
−5.2000

−1.15

1
1
1
1
1

2
2
2

1 1 2 2 0.3536 −2.83

1
1

2 1

1

1 0.5196

0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.5196
0.3536
0.5196
0.5196
0.5196
0.5196
0.5196

−10.01 <.0001

< 2

2
2

2 − 5.0000 − 9.62 .0001

∗ gender ∗ attract − 1.2000 − 2.31

2
2
2
2
2
2

1
1

2
2

1 0.0346

age ∗ gender ∗ attract − 4.2000 − 8.08 < .0001

2

age ∗ gender ∗ attract 1

1
1
1

2
2
2
2

2 1

1

1 − 4.2000 − 8.08 < .0001

age ∗ gender ∗ attract − 4.0000 − 7.70 < .0001

2
2
2

2

age ∗ gender ∗ attract

− 0.2000 − 0.38

2
2

1 0.7054

age attract

age ∗ gender ∗ attract
age ∗ gender ∗ attract
age

∗ gender ∗

− 3.2000 − 6.16 < .0001

2

− 0.2000

2
2
2
2
2
2

1
1
1

1 2 1 2 0.57

7.70
1.92
7.31
1.54

0.5795

< .0001

1
1

2
2
2
2
2

2
2
2
2
2

1 4.0000

1.0000
3.8000
0.8000

attract
age ∗ gender ∗ attract
age ∗ gender ∗ attract
age

∗ gender ∗

2 0.0723

1
1

2
2

1 < .0001

2
2

0.1432

∗ gender ∗ attract

−3.0 000 − 8.49 < .0001

2 1

Figure 14.31 A portion of the output from the simple effects tests.