Complex Mixed Design: One Between-Subjects Factor and Two Within-Subjects Factors - Analysis of Variance Designs - page 448

This Table presents the Bonferroni
adjusted p -values.

Differences of Least Squares Means

Effect
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype
gender ∗ color ∗ toytype

gender color toytype gender color toytype Adjustment Adj P

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
2
2
2

1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
1
1
1
1
1
2

1
1
1
1
1
1
1
2
2
2
2
2
2
1
1
1
1
1
2
2
2
2
1
1
1
2
2
1

1
1
1
2
2
2
2
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2

1
2
2
1
1
2
2
2
2
1
1
2
2
2
1
1
2
2
1
1
2
2
1
2
2
2
2
2

2
1
2
1
2
1
2
1
2
1
2
1
2
2
1
2
1
2
1
2
1
2
2
1
2
1
2
2

Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni
Bonferroni

0.1487
1.0000
1.0000
0.2483
1.0000
1.0000
1.0000
0.0067
0.0304
1.0000
0.1309
0.6789
0.9574
1.0000
0.0118
1.0000
0.4082
0.2927
0.0528
1.0000
1.0000
1.0000
0.1065
0.6084
0.8777
1.0000
1.0000
1.0000

Figure 15.33 Bonferroni corrected tests of significance between means.

Although SAS and SPSS frequently yield repeated measures adjusted prob-
abilities that lead researchers to the same conclusions, this is one of those
times when they do not. In this instance, we would be inclined to abide
by the SPSS results, since the total number of paired comparisons (which
SAS uses as its count to perform the Bonferroni correction) is more than
we intended to deal with when we configured our simple effects strategy.