Independent Samples T-Test Assignment Help — SPSS Steps and Levene's Test

What Is the Independent Samples T-Test and When Do You Use It?

The independent samples t-test compares the means of a continuous variable between two separate, unrelated groups — for example, test scores between a control group and a treatment group, or satisfaction ratings between two different customer segments. Use it when you have one categorical independent variable with exactly two levels and one continuous dependent variable, measured on different people in each group.

If you’re comparing more than two groups, you need one-way ANOVA instead. If the same people are measured twice, you need the paired samples t-test instead.

Assumptions You Must Check Before Running It in SPSS

Two assumptions matter beyond the basic design requirements:

• Normality. The dependent variable should be approximately normally distributed within each group. Check this with a Shapiro-Wilk test (Analyze > Descriptive Statistics > Explore, with Normality plots requested) — a p > .05 suggests the assumption is reasonably met.
• Homogeneity of variance. The two groups should have similar variances. SPSS tests this automatically with Levene’s Test for Equality of Variances, shown directly in the t-test output table.

This second assumption is where most students go wrong — not because the test is hard to run, but because the output table has two rows and picking the wrong one silently gives you the wrong answer.

How to Run It in SPSS (Step by Step)

1. Go to Analyze > Compare Means > Independent-Samples T Test.
2. Move your continuous outcome into the Test Variable(s) box.
3. Move your two-group categorical variable into the Grouping Variable box, then click Define Groups and enter the two group codes (e.g. 1 and 2).
4. Click OK.

SPSS produces two tables: Group Statistics (means, SDs, and sample sizes per group) and Independent Samples Test (Levene’s test plus the t-test itself).

How to Interpret the Output

In the Independent Samples Test table:

1. Look at Levene’s Test for Equality of Variances first. If its p-value is greater than .05, variances are equal — read the “Equal variances assumed” row. If it’s .05 or below, read the “Equal variances not assumed” row (this uses the Welch correction).
2. From the row you selected, read the t-value, degrees of freedom (df), and Sig. (2-tailed) — this is your p-value.
3. Calculate or read the effect size, Cohen’s d, which tells you the practical size of the difference: 0.2 is a small effect, 0.5 is medium, 0.8 is large.

How to Report the Results in APA Format

A complete APA-style report includes the test statistic, degrees of freedom, exact p-value, means and standard deviations for both groups, and the effect size:

An independent samples t-test found that treatment-group scores (M = 78.4, SD = 6.2) were significantly higher than control-group scores (M = 71.9, SD = 7.1), t(58) = 3.72, p < .001, d = 0.97.

Independent Samples T-Test vs Paired Samples T-Test: Understanding the Difference

The test names sound similar but answer different questions. The independent samples t-test compares two different groups of people. The paired samples t-test compares the same people measured twice — before and after an intervention, for example. Using the independent version on paired data (or vice versa) is one of the most common assignment errors, because both produce a t-value and a p-value that look plausible even when the test is wrong for the design.

Not sure which one your dataset needs? See the full SPSS statistical test guide, or get help with this specific assignment.