Introduction to SPSS Independent Samples T-Test

The Independent Samples T-Test is a powerful statistical test that allows comparison between two different groups. In this tutorial, we will learn how to perform an Independent Samples T-Test using SPSS software and explain it with a practical example.

Data Details

• Dependent Variable: Test Score (Continuous variable, in the form of marks)
• Independent Variable: Teaching Method (Categorical variable, with two groups: Method A, Method B)

Our data looks something like this:

Now we will perform the Independent Samples T-Test in SPSS and check whether there is a significant difference in test scores between these teaching methods.

1 Data Entry in SPSS

1. Open SPSS: Launch the SPSS software and create a new dataset.
2. Define Variables:
   • Variable 1: Test_Score (Type: Numeric, Label: Test Score)
   • Variable 2: Teaching_Method (Type: Numeric, Label: Teaching Method, Values: 1 = Method A, 2 = Method B)
3. Enter Data:
   • Enter the scores given above in the Test_Score column.
   • In the Teaching_Method column, enter the code (1 or 2) corresponding to each score’s method.

The above shows how the Data View in SPSS will look.

2 Performing the Independent Samples T-Test

1. Go to Menu:
   • In the SPSS menu bar, click on Analyze > Compare Means > Independent-Samples T Test.
2. Select Variables:
   • The Independent-Samples T Test dialog box will open.
   • Move Test_Score to the Test Variable(s) box (click and use the right arrow).
   • Move Teaching_Method to the Grouping Variable box.
3. Define Groups:
   • Click on the Define Groups button.
   • Enter 1 (Method A) for Group 1 and 2 (Method B) for Group 2.
   • Click Continue.
4. Set Options:
   • Click on the Options button.
   • Keep the Confidence Interval Percentage at 95% (default).
   • Missing Values: “Exclude cases analysis by analysis” should be selected.
   • Click Continue.
5. Click OK:
   • After confirming all settings, click OK.
   • The results will appear in the SPSS Output window.

3 Analyzing the Output

The SPSS output will show two main tables. We will understand them:

Group Statistics Table

This table shows the mean, standard deviation, and sample size for both groups.

Interpretation: The mean score for Method A (88.40) is higher than Method B (80.00).

Independent Samples Test Table

This table shows the T-Test results and significance.

It has two rows:

• Equal variances assumed: When variances are equal in both groups.
• Equal variances not assumed: When variances are not equal.

Interpretation:

1. Levene’s Test: Levene’s Test checks the equality of variances. Here Sig. = 0.409 (> 0.05), so we assume variances are equal. Therefore, we will use the Equal variances assumed row.
2. T-Test Result:
   • t-value = 4.873, degrees of freedom (df) = 8, p-value (Sig. 2-tailed) = 0.001.
   • p-value (0.001) < 0.05, so the null hypothesis (that the means of both groups are equal) is rejected.
   • Mean Difference: Method A scores are on average 8.400 points higher than Method B.

4 Reporting Results (in APA Style)

To report results in APA format, you can write:

Checking T-Test Assumptions

It is essential to check some assumptions for the Independent Samples T-Test:

1. The dependent variable should be continuous: Test scores are continuous, so this assumption is met.
2. The independent variable should be categorical: Teaching method is categorical (2 groups), so this is also met.
3. Independence of observations: Each student’s score is independent, so this assumption is met.
4. No significant outliers: Check using a boxplot (Graphs > Chart Builder > Boxplot). If there are no extreme values, the assumption is met.
5. Normality: Check using the Shapiro-Wilk test (Analyze > Descriptive Statistics > Explore > Plots > Normality plots with tests). If p > 0.05, the data is approximately normal.
6. Homogeneity of variances: Already checked with Levene’s Test (Sig. > 0.05).

Suggestions and Tips

• Data Backup: Always keep a backup of your data.
• Save Syntax: Learn to save syntax in SPSS so you can rerun the same analysis.
• Visualize Results: Create boxplots or bar graphs (Graphs > Chart Builder) to make results visually clear.
• Practice: Practice with different datasets to understand SPSS features.