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## The t-test with SPSS
The t-test is used for testing differences between two means. In order to use
a t-test, the A t-test for independent groups is useful when the same variable has been measured in two independent groups and the researcher wants to know whether the difference between group means is statistically significant. "Independent groups" means that the groups have different people in them and that the people in the different groups have not been matched or paired in any way. A t-test for related samples or a t-test for dependent means is the appropriate test when the same people have been measured or tested under two different conditions or when people are put into pairs by matching them on some other variable and then placing each member of the pair into one of two groups.
A t-test for independent groups is useful when the researcher's
goal is to compare the difference between means of two groups on the same variable.
Groups may be formed in two different ways. First, a preexisting characteristic
of the participants may be used to divide them into groups. For example, the
researcher may wish to compare college GPAs of men and women. In this case,
the Like all other statistical tests using SPSS, the process begins with data. Consider the fictional data on college GPA and weekly hours of studying used in the correlation example. First, let's add information about the biological sex of each participant to the data base. This requires a numerical code. For this example, let a "1" designate a female and a "2" designate a male. With the new variable added, the data would look like this:
With this information added to the file, two methods of dividing participants
into groups can be illustrated. Note that Participant #05 has just a single
dot in the column for sex. This is the standard way that SPSS indicates missing
data. This is a common occurrence, especially in survey data, and SPSS has flexible
options for handling this situation. Begin the analysis by entering the new
data for sex. Use the arrow keys or mouse to move to the empty third column
on the spreadsheet. Use the same technique as previously to enter the new data.
When data is missing (such as Participant #5 in this example), hit the > Define Variable and
type in the name of the variable, "Sex." Then go to "value"
And type a "1" in the box. For "Value Label," type "Female."
Then click on ADD. Repeat the sequence, typing "2" and "male"
in the appropriate boxes. Then click ADD again. Finally, click CONTINUE
>OK and you will be back to the main SPSS menu.To request the t-test, click Test Variable(s)" box. Then highlight Sex in
the left box and click the bottom arrow (pointing right) to transfer sex to
the "Grouping Variable" box. Then click Define Groups.
Type "1" in the Group 1 box and type "2" in the Group 2
box. Then click Continue. Click Options and you will see the confidence
interval or the method of handling missing data can be changed. Since the default
options are just fine, click Continue > OK and the results will quickly
appear in the output window. Results for the example are shown below:
The output begins with the means and standard deviations for the two variables
which is key information that will need to be included in any related research
report. The "Mean Difference" statistic indicates the magnitude of
the difference between means. When combined with the confidence interval for
the difference, this information can make a valuable contribution to explaining
the importance of the results. "Levene's Test for Equality of Variances"
is a test of the homogeneity of variance assumption. When the value for A second method of performing an independent groups t-test with SPSS is to
use a noncategorical variable to divide the test variable (college GPA in this
example) into groups. For example, the group of participants could be divided
into two groups by placing those with a high number of study hours per week
in one group and a low number of study hours in the second group. Note that
this approach would begin with exactly the same information that was used in
the correlation example. However, converting the Studyhrs data to a categorical
variable would cause some detailed information to be lost. For this reason,
caution (and consultation) is needed before using this method. To request the
analysis, click Define Groups... and click the button.
Enter a value (20 in this case) into the box. All participants with values less
than the cutpoint will be in one group and participants with values greater
than or equal to the cutpoint will form the other group. Click Cut pointContinue
> OK and the output will quickly appear. The results from the example
are shown below:
The "Group Statistics" table provides the means and standard deviations along with precise information regarding the formation of the groups. This can be very useful as a check to ensure that the cutpoint was selected properly and resulted in reasonably similar sample sizes for both groups. The remainder of the output is virtually the same as the previous example.
The t-test for
One thing to note about the new data is that the GPA of the first participant is missing. Given the 1.8 GPA at the first assessment, it seemed reasonable that this person might not remain in college for the entire four years. This is a common hazard of repeated measures designs and the implication of such missing data needs to be considered before interpreting the results. To request the analysis, click > Paired-Samples T Test .... A window will
appear with a list of variables on the left and a box labeled "Paired Variables"
on the right. Highlight two variables (Colgpa and Colgpa2, in this example)
and transfer them to the "Paired Variables" box by clicking the right-pointing
arrow between the boxes. Several pairs of variables can be entered at this time.
The Options... button opens a window that allows control of the confidence
interval and missing data options. Click Continue (if you opened the
Options... window) > OK to complete the analysis. The
output will appear in an Output window. Results for the example problem are
shown below:
The output is similar to the independent groups t-test. The first table of
the output shows the means and standard deviations for the two groups and the
second table shows the correlation between the paired variables. The next table
shows the mean of the differences, standard deviation of the differences, standard
error of the mean, the confidence interval for the difference, and the obtained
value for t. The 2-tailed Sig[nificance] which is stated as a probability
is shown in the last table. As usual, probabilities |

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