Under Variable view (see tab at bottom of page). It should
look like:
|
Name
|
Type
|
Width
|
Decimals
|
Label
|
Values
|
Ignore the rest
|
|
Condition
|
numeric
|
8
|
0
|
Condition
|
*(see below)
|
Ignore the rest
|
|
score
|
numeric
|
8
|
0
|
Score
|
None
|
Ignore the rest
|
Go back to Data View and enter the following:
|
Condition
|
Score
|
|
1
|
5
|
|
1
|
3
|
|
1
|
7
|
|
1
|
2
|
|
1
|
6
|
|
2
|
6
|
|
2
|
7
|
|
2
|
9
|
|
2
|
10
|
|
2
|
8
|
|
3
|
4
|
|
3
|
8
|
|
3
|
7
|
|
3
|
9
|
|
3
|
5
|
Go to Analyze/ General Linear Model/ Univariate. Move your
score variable into Dependent Variable. Move condition into Fixed Factors. Go
to Post Hoc (button on right side of variable screen), move condition into
right box for post hoc tests. For now, choose Tukey's test (note, there are
many different post hoc tests, check a stats book or the SPSS book listed below
to choose the correct one for your data). Press continue to return to the
variable screen. Go to Options, move Overall and condition to right to display
means. Also, check descriptive statistics. Press continue to return to the
variable screen. Press ok.
Your results should look like the following:
|
Descriptive Statistics
|
|||
|
Dependent Variable: Score
|
|||
|
Condition
|
Mean
|
Std. Deviation
|
N
|
|
control
|
4.60
|
2.074
|
5
|
|
group A
|
8.00
|
1.581
|
5
|
|
group B
|
6.60
|
2.074
|
5
|
|
Total
|
6.40
|
2.293
|
15
|
|
Tests of Between-Subjects Effects
|
|||||
|
Dependent Variable: Score
|
|||||
|
Source
|
Type III Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
Corrected Model
|
29.200a
|
2
|
14.600
|
3.946
|
.048
|
|
|
614.400
|
1
|
614.400
|
166.054
|
.000
|
|
condition
|
29.200
|
2
|
14.600
|
3.946
|
.048
|
|
Error
|
44.400
|
12
|
3.700
|
|
|
|
Total
|
688.000
|
15
|
|
|
|
|
Corrected Total
|
73.600
|
14
|
|
|
|
|
a. R Squared = .397 (Adjusted R Squared = .296)
|
|||||
|
Multiple Comparisons
|
||||||
|
Dependent Variable: Score
Tukey HSD
|
||||||
|
(I) Condition
|
(J) Condition
|
Mean Difference (I-J)
|
Std. Error
|
Sig.
|
95% Confidence Interval
|
|
|
Lower Bound
|
Upper Bound
|
|||||
|
control
|
group A
|
-3.40*
|
1.217
|
.040
|
-6.65
|
-.15
|
|
group B
|
-2.00
|
1.217
|
.266
|
-5.25
|
1.25
|
|
|
group A
|
control
|
3.40*
|
1.217
|
.040
|
.15
|
6.65
|
|
group B
|
1.40
|
1.217
|
.503
|
-1.85
|
4.65
|
|
|
group B
|
control
|
2.00
|
1.217
|
.266
|
-1.25
|
5.25
|
|
group A
|
-1.40
|
1.217
|
.503
|
-4.65
|
1.85
|
|
|
Based on observed means.
The error term is
Mean Square(Error) = 3.700.
|
||||||
|
* The mean difference is significant at the .05 level.
|
||||||
What does this mean? There is a significant difference between the 3 groups (see where I marked in blue above). This does not tell which groups are different from each other, to find this out we did Tukey's post hoc tests and found that the control varied significantly from group A (see data marked in yellow above), no other groups differed. So let's write it up as you would in your paper:
An independent ANOVA was conducted comparing the control
group (M = 4.6; SE = .86) to group A
(M = 8.0; SE = .86) and group B (M = 6.6; SE = .86). The result (F(2, 12) =3.95, p= .048) indicates that there is a significant difference between
the groups and the null hypothesis is rejected. Tukey's post hoc tests examined
which groups differed. It was found that only the control group and group A
differed (p< .05).
A great resource for SPSS is
Pallant, J. (2013). The
SPSS Survival Manual, 5th edition. Open University Press.
Next time, we will look at repeated measures ANOVA. Do you have an issue or a
question that you would like me to discuss in a future post? Send me an email
with your ideas. leann.stadtlander@waldenu.edu
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