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MODULE 0 2
NONPARAMETRIC TEST WITH SPSS
Test statistics
CHI SQUARE
Data. Use ordered or unordered numeric categorical variables
(ordinal or nominal levels ofmeasurement).
PROCEDURES
1. Click on Analyze from the menu bar.
2. Click on Descriptive Statistics from the pull-down menu.
3. Click on Crosstabs to open the Crosstabs dialog box.
4. Click on the name of the row variable (gender) and the top
right arrow button.
5. Click on the name of the column variable (goals) and the
middle right arrow button.
6. Click on the Cells button to open the Crosstabs: Cell Display
dialog box.
7. Click on Row in the Percentages box to indicate that you want
percentages by gender (the row
variable).
8. Click on Continue.
9. Click on the Statistics button to open the Crosstabs:
Statistics dialog box.
10. Click on Chi-Square.
11. Click on Continue.
12. Click on OK.
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One sample t test
Data. To test the values of a quantitative variable against a
hypothesized test value, choose a
quantitative variable and enter a hypothesized test value.
PAIR SAMPLE T TEST
Data. For each paired test, specify two quantitative variables
(interval level of measurement or
ratio level of measurement). For a matched-pairs or case-control
study, the response for each test
subject and its matched control subject must be in the same case
in the data file.
Enter OR opening the data file:
1. Click on Analyze from the menu bar.
2. Click on Compare Means from the pull-down menu.
3. Click on Paired-Samples T Test from the pull-down menu. This
opens the Paired-Samples T
Test dialog box .
4. Click on the before variable. It will appear in the Current
Selections box as Variable 1.
5. Click on the after variable. It will appear in the Current
Selections box as Variable 2.
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6. Move the paired variables into the Paired Variables box by
clicking on the right arrow
button.
7. Click on OK to run the procedure.
Independent sample t test
Data. The values of the quantitative variable of interest are in
a single column in the data file.
The procedure uses a grouping variable with two values to
separate the cases into two groups.
The grouping variable can be numeric (values such as 1 and 2 or
6.25 and 12.5) or short string
(such as yes and no). As an alternative, you can use a
quantitative variable, such as age, to split
the cases into two groups by specifying a cutpoint .
Assumptions. For the equal-variance t test, the observations
should be independent, random
samples from normal distributions with the same population
variance. For the unequal-variance t
test, the observations should be independent, random samples
from normal distributions. The
two-sample t test is fairly robust to departures from normality.
When checking distributions
graphically, look to see that they are symmetric and have no
outliers.
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Procedures
1. Click on Analyze from the menu bar.
2. Click on Compare Means from the pull-down menu.
3. Click on Independent Samples T-Test from the pull-down menu
to open the Independent-
Samples T Test dialog box .
4. Click on and move the temp variable the Test Variable(s) box
using the upper right arrow
button.
5. Click on and move the sex variable to the Grouping Variable
box using the lower right
arrow button.
6. Notice that two question marks appear in parentheses after
the variable sex. This signifies
that you need to indicate the two values of the class variable
for which you wish to calculate
mean differences. To do so, click on Define Groups to open the
Define Groups dialog box.
7. In our example, females are coded 0 and males are coded 1.
Therefore, enter these numbers in
the Group 1 and Group 2 boxes. (The cut point option is used if
there are more than two values
of the grouping variable.)
8. Click on Continue to close the dialog box.
9. Click on OK to run the procedure.
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OUTPUT
To Obtain Tests for Several Independent Samples
This feature requires the Statistics Base option.
From the menus choose:
Analyze
Nonparametric TestsLegacy Dialogs
K Independent Samples...
Select one or more numeric variables.
Select a grouping variable and click Define Range to specify
minimum and maximuminteger values for the grouping variable.
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KRUSKA WALIS
Data. Use numeric variables that can be ordered.
Assumptions. Use independent, random samples. The Kruskal-Wallis
H test requires that the
tested samples be similar in shape
Mann-Whitney Test
Moses Test
Kolmogorov-Smirnov TestData. Use quantitative variables
(interval or ratio level of measurement).
Wald-Wolfowitz Test
Median Test
Sign Test
Compiled by Oloyede I/08053049890
Research Embassy @http://doctoralresearch.gnbo.com.ng
http://doctoralresearch.gnbo.com.ng/http://doctoralresearch.gnbo.com.ng/http://doctoralresearch.gnbo.com.ng/http://doctoralresearch.gnbo.com.ng/
