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In-Depth Information
Examining the lower table shows that the
p
value associated with that
mean difference is .2328, a value that we would judge to be not statistically
significant. Translated into more meaningful prose, males and females
living in small towns do not differ in their degree of expressed loneliness.
As indicated in Section 8.12.3, results of the simple effects tests need to be
examined with respect to the graph of the means so that these comparisons
can be taken in context.
8.17 COMMUNICATING THE RESULTS
A written summary of the results is as follows:
3 between subjects design using gender (male and female) and size of
residential community (large city, small town, and rural community) evaluated
the degree to which participants experienced feelings of loneliness. While the
main effect of gender,
F
(1, 24)
A2
×
2
=
38
.
03,
p
<.
05,
η
=
.
27, and the main
2
effect of residential community,
F
(2, 24)
=
21
.
06,
p
<.
05,
η
=
.
30, were
both significant, these were superceded by the significant Gender
×
Residential
2
Community interaction,
F
(2, 24)
27.
The interaction is presented in Figure 1. Simple effects tests were performed
by using a Bonferroni adjustment to hold the alpha level at .05. Females were
lonelier in both small towns and rural communities than they were in large
cities. Males were lonelier in small towns than in either large cities or rural
communities. Furthermore, although both groups were relatively lonely in
small towns, females were significantly lonelier in that environment than were
males. In contrast, females were quite lonely in rural communities, but males
in that environment were not very lonely at all.
=
19
.
16,
p
<.
05,
η
=
.
CHAPTER 8 EXERCISES
8.1.
Male and female community mental health clients (Factor
a
)were
randomly assigned to the following three treatment groups (Factor
b
):
Psychoanalytic, cognitive-behavioral, and brief psychotherapy. GAF scores
at the end of four weeks of treatment served as the dependent measure. The
data were as follows:
a
1
b
1
a
1
b
2
a
1
b
3
a
2
b
1
a
2
b
2
a
2
b
3
55
62
65
55
70
75
45
60
65
55
65
70
40
65
70
60
60
65
35
70
70
50
60
60
52
68
60
55
65
60
50
60
60
53
65
65
48
60
65
50
70
70
50
55
65
38
68
70
a.
Conduct an ANOVA on these data by hand and with SPSS or SAS.
