Information Technology Reference
In-Depth Information
a. Conduct an ANOVA on these data with SPSS or SAS.
b. If the interaction(s) is(are) statistically significant, conduct simple
effects analyses and multiple comparisons tests with SPSS or SAS.
15.2.
Assume the management of a large hotel chain was interested in
evaluating the effect of type of view (Factor
A
) and time of day of eval-
uation (Factor
B
) on customer satisfaction over a four-day period (Fac-
tor
C
). The between-subjects factor was Factor
A
,typeofview(
a
1
=
ocean and
a
2
=
parking lot). The within-subjects factor were Factor
B
, time
of day (
b
1
early afternoon), and Factor
C
,a
blockoffourcontiguousdaysthatthe
n
=
early morning and
b
2
=
3 customers spent at the hotel.
The dependent measure was the rating on an overall satisfaction question
(1
=
extremely unsatisfied
to 10
extremely satisfied
). The data are as follows:
=
b
1
b
2
b
1
b
2
b
1
b
2
b
1
b
2
Subject
c
1
c
2
c
3
c
4
a
1
s
1
8
8
8
8
9
8
9
8
s
2
9
8
8
7
8
9
10
9
s
3
8
8
8
8
9
8
10
8
a
2
s
4
3
2
2
2
3
2
1
1
s
5
4
2
2
1
1
1
1
2
s
6
3
2
2
1
1
1
1
1
a. Conduct an ANOVA using SPSS or SAS.
b. If the interaction(s) is(are) statistically significant, conduct simple
effects analyses and multiple comparisons tests with SPSS or SAS.
15.3.
Assume the following 2
3 factorial data set with Factor
A
as the
between-subjects factor and Factors
B
and
C
as the within-subjects factors,
and
n
×
3
×
=
4 participants per treatment combination.
b
1
b
2
b
3
b
1
b
2
b
3
b
1
b
2
b
3
Subject
c
1
c
2
c
3
a
1
s
1
5
1
2
10
9
7
15
13
15
s
2
4 6 2 99 9 6 2 5
s
3
3 5 3 88 8 7 8 7
s
4
2 4 1 78 9 8 4 7
a
2
s
5
1 5 1 45 6 0 1 3
s
6
2 4 1 34 7 0 1 3
s
7
3
3
2
3
5
5
9
12
12
s
8
4 2 2 25 6 0 1 2
a.
Conduct an ANOVA on these data using SPSS or SAS.
b.
If the interaction(s) is(are) statistically significant, conduct simple
effects analyses and multiple comparisons tests with SPSS or SAS.
