SPSS Analysis Using General Linear Model – Repeated Measures
Solitary Confinement Experiment
The data are from an experiment run to evaluate the effect of solitary confinement on
brain activity of prisoners, i.e. frequency of brain waves. There are two factors of
interest: the between subjects factor (Solitary Confinement – Yes/No) and the within
subjects factor (Day – 1, 4, 7). The subjects, prisoners, are repeatedly measured on
days 1, 4, and 7. There are five columns in the SPSS Data Table: Solitary, Prisoner,
Day1, Day4 and Day7.
Solitary
Confinement
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
Prisoner
Day 1
Day 4
Day 7
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
14
24
21
20
15
17
16
10
6
32
20
16
15
20
14
13
4
22
21
13
7
16
10
14
5
6
11
8
3
20
20
15
17
17
16
13
6
22
21
12
6
14
7
13
2
3
9
5
0
19
17
17
14
19
15
13
6
21
22
14
You will need to use Analyze – General Linear Model – Repeated Measures
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•
•
•
•
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Within subject factor name: Day
Number of Levels: 3
Click on Define
Add Day1, Day4 and Day7 as Within-Subjects Variables.
Add Solitary as the Between-Subjects Factor(s).
The Model should be Full Factorial (this is the default).
Click on Post Hoc and select LSD and put Solitary under Post Hoc tests for.
Click on Continue.
Click on OK.
1
Some of the output from this analysis appears below.
Tests of Within-Subjects Effects
Measure: MEASURE_1
Source
Day
Day * Solitary
Error(Day)
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Type III Sum
of Squares
256.900
256.900
256.900
256.900
260.433
260.433
260.433
260.433
102.000
102.000
102.000
102.000
df
2
1.467
1.653
1.000
2
1.467
1.653
1.000
36
26.401
29.759
18.000
Mean
Square
128.450
175.153
155.387
256.900
130.217
177.562
157.524
260.433
2.833
3.864
3.428
5.667
F
45.335
45.335
45.335
45.335
45.959
45.959
45.959
45.959
Sig.
.000
.000
.000
.000
.000
.000
.000
.000
Rather than doing comparisons, SPSS automatically calculates linear and quadratic
contrasts for the within-subjects factor. The two F-tests indicate that both the linear and
quadratic contrasts are statistically significant. This means that as Day increases, mean
brain wave frequency changes in a way that is both linear and curved.
Tests of Within-Subjects Contrasts
Measure: MEASURE_1
Source
Day
Day * Solitary
Error(Day)
Day
Linear
Quadratic
Linear
Quadratic
Linear
Quadratic
Type III Sum
of Squares
235.225
21.675
235.225
25.208
66.050
35.950
df
1
1
1
1
18
18
Mean Square
235.225
21.675
235.225
25.208
3.669
1.997
F
64.104
10.853
64.104
12.622
Sig.
.000
.004
.000
.002
Tests of Between-Subjects Effects
Measure: MEASURE_1
Transformed Variable: Average
Source
Intercept
Solitary
Error
Type III Sum
of Squares
11426.400
248.067
1610.200
df
1
1
18
Mean Square
11426.400
248.067
89.456
F
127.733
2.773
Sig.
.000
.113
Warnings
Post hoc tests are not performed for Solitary because there are fewer than three groups.
2