The independent-samples t-test, or independent t-test, compares the means of two unrelated groups for the same continuous dependent variable.
To determine whether test anxiety varies depending on educational level, for instance, you could perform an independent t-test (i.e., your dependent variable would be “test anxiety” and your independent variable would be “educational level”, which has two groups: “undergraduates” and “postgraduates”).
You can do an independent t-test using SPSS Statistics, analyze the results, and report them using the instructions in this “fast start” guide.
Test Procedure in SPSS Statistics
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- Click Analyze > Compare Means > Independent-Samples t-Test…on the top menu, as shown below:
The Independent-Samples T Test dialogue box will appear, as seen below:
2. By selecting the necessary variables and clicking the SPSS Right Arrow Button, you may move the dependent variable, Cholesterol, into the Test Variable(s): box and the independent variable, Treatment, into the Grouping Variable: box. The last screen you see will look like this:
3. Next, you must define the groupings (treatments). Select the Define Options Button by clicking it. The Define Groups dialogue box will then appear, as illustrated below:
4. In the Group 1: box, type “1,” and in the Group 2: box, type “2.” Recall that the Exercise Treatment group was labeled as 1, and the Diet Treatment group as 2
If your study includes more than two treatment groups but you just want to compare two of them, for example, the diet and drug treatment groups, you can write 1 into the Group 1: box and 3 into the Group 2: box (i.e., if you wished to compare the diet with drug treatment).
5. Click the 
button.
6. Click the Options Button button to adjust the confidence level restrictions or how cases are excluded. Following will be displayed to you:
7. Click the button. Your window for the Independent-Samples t-Test will reappear.
8. Click the 
button.
Output of the independent t-test in SPSS Statistics
For the independent t-test, SPSS Statistics creates two primary tables of output.
Table of Group Statistics
The mean and standard deviation for the two groups you compared are provided in this table along with other helpful descriptive data.
It would be regarded usual to provide information on the mean and standard deviation for this data, unless you have other reasons to do so. You might also mention how many people were in each of the two groups. When you have missed values and more individuals were recruited than could be analyzed, this can be helpful.
You can also use a diagram to visually display your results. You could, for instance, use a bar chart with error bars (e.g., where the error bars could use the standard deviation, standard error or 95 percent confidence intervals). Others may find it simpler to grasp your results if you do this. Once more, we demonstrate how to do this in our improved independent t-test guide.
Table for Independent Samples Testing
The actual outcomes of the independent t-test are shown in this table.
As you can see, the result in the “Sig. (2-tailed)” row is less than 0.05, indicating that the group means are statistically different from one another. According to the Group Statistics table, participants in the exercise trial had lower cholesterol levels at the conclusion of the program than participants in the calorie-restricted diet.
Reporting the independent t-results test’s
Based on the aforementioned findings, you might report the study’s findings as follows (note that this does not include the findings from your testing of assumptions or estimations of impact size):
In this study, it was discovered that male participants who were overweight and inactive had statistically significantly lower cholesterol levels (5.80 ± 0.38 mmol/L) at the end of an exercise-training programme compared to after a calorie-controlled diet (6.15 ± 0.52 mmol/L), t(38)=2.428, p=0.020.
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