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MEANS HANDOUT
Suppose you have THREE different groups instead of TWO. This might consist
of a control group and two different treatment groups. Again, you would
enter the scores on the dependent variable in one column under one
variable name, and you would create another variable that would be a
"dummy" variable containing a 1, 2, or 3 to designate which group each
score came from. So, it might look like this:
55
45
39
41
44
50
1
1
2
2
3
3
What this means is that the 55 and the 45 come from group 1, the 39 and 41
come from group 2, and the 44 and 50 come from group 3.
Now, we would like to have means and standard deviations FOR EACH OF THE
THREE GROUPS. However, if you run DESCRIPTIVES or FREQUENCIES, SPSS will
combine all six scores, and give you the mean of that 6-score
distribution, the SD, etc.
That is good to help us find any errors in our data entry, but is of
little use in studying the relative performance of the three groups.
MEANS, however, can be used to get separate statistics for each of the
three groups.
_____ 1. The first step, of course, is to open SPSS, name the variables,
and enter the data. Don't forget to stipulate value labels for the dummy
variable. Make 1 stand for Treatment #1, 2 for Treatment #2, and 3 for
Control Group. It should look like this:
_____ 2. Now, simply click on Analyze, Compare Means, and Means. The Means
Box will open:
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_____ 3. Now, we need to move the variables into either the Dependent List
field or the Independent List field by highlighting and clicking the
correct RIGHT ARROW button.
_____ 4. Click the Options button at the bottom of the box. The following
Means: Options box will appear, and should be made to look like the
following:
The way this works is that you select the statistics you want from the
column on the left (the Statistics field) and move them to the column at
the right (the Cell Statistics field) by clicking the RIGHT ARROW button
in the middle of the box.
_____ 5. Be sure to put a check mark in the box next to Anova table and
eta.
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_____ 6. Click the Continue button and then the OK button to run the
analysis.
Here is what the output will look like:
The first box merely shows that all six cases are included.
The second box, labeled REPORT, includes the statistics we ordered FOR
EACH OF THE THREE VARIABLES and for the total distribution (all 6 cases
collapsed into one variable).
Just from eyeballing the data, it appears that Treatment #1 might be best,
the Control Group came in second, and Treatment #2 was poorest.
The box labeled ANOVA Table is the statistical test to see if the
differences in the means are significant. Notice the F value for
differences among means is 2.257, and the Sig. is .252. Since the
significance of .252 is NOT .05 or smaller, the test was NOT SIGNIFICANT,
the means do not differ, we cannot reject the null hypothesis of equal
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means, and we have no evidence that any of these three treatments produce
different results.
The box labeled Measures of Association reports Eta Squared, which is the
proper effect size statistic for use with ANOVA. Since there were no
significant differences among the means, effect size is not appropriate,
and we should ignore this.
________________________________
Means has done everything we asked.
The problem is that the ANOVA that is produced is pretty sketchy.
It lacks a test for homogeneity of variance.
And it lacks a multiple comparison test.
For those, we have to use a different analysis.
But MEANS is a good first run.
If we get get differences in the ANOVE, we can then check homogeneity of
variance and run a multiple comparison using a different SPSS routine.
________________________________
Of course, three groups with only two scores in each group is not very
realistic. Usually, our data file is much bigger and more complex.
Suppose you have data on three variables that are scores on three
different tests, and a break variable (dummy variable) called "group" to
identify experimental group, control group, and second control group
subjects.
We might name the first variable (scores on the first test) as test1, the
second as test2, and the third as test3. Again, the break variable is
called group.
So our scores might look like this (means.procedure.sav):
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75 86 88 1
72 80 84 1
74 78 88 1
80 80 90 1
72 82 90 1
80 90 99 2
82 88 98 2
79 86 96 2
80 90 98 2
79 89 97 2
80 91 98 3
79 92 97 3
81 90 96 3
79 91 99 3
80 90 99 3
Reading across the numbers in each row, they are for test1, then test2,
test3, and group.
Enter these scores in SPSS for Windows. Name the variables as usual (don't
forget value labels for the group variable). Make the value labels 1 for
experimental group, 2 for control group, and 3 for second control group.
Run three MEANS procedures - one for test1, one for test2, and one for
test3. (You can order all three at once simply by moving all three
variable names into the Dependent List field in the Means box as follows:)
Study the output and answer the following questions:
1. What are the means for test1? _______________
2. According to the ANOVA, are there differences in the means for test1?
_____ yes
_____ no
3. How do you know?____________________________________________________
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_______________________________________________________________________
_____________________________________________________________________
4. Look at the ANOVA for test2. Does this analysis indicate significant
differences among the three means? _____ yes
_____ no
5. Look at the ANOVA for test3. Does this analysis indicate significant
differences among the three means? _____ yes
_____ no
6. Does MEANS test for homogeneity of variance, or give you a multiple
comparison test? _____ yes
_____ no
7. PRINT copies of the data screen and the output screen. Write the
answers to the above on this paper. Staple the entire packet and turn it
in, labeling it MEANS HOMEWORK, WEEK #3.
END