How to Run a Paired Samples t Test in SPSS (2026) Guide

To run a paired samples t test in SPSS, go to Analyze > Compare Means and Proportions > Paired-Samples T Test, or Analyze > Compare Means > Paired-Samples T Test in SPSS 28 and earlier. Move your two related variables into the Variable 1 and Variable 2 boxes and click OK.

That is the whole procedure, and it takes about two minutes once your data are set up correctly. The part that actually trips people up is everything before the menu: making sure each row holds the same person, putting two genuinely related scores in the pair boxes, and knowing what the three output tables are telling you afterwards.

This guide walks through the full workflow in the order SPSS asks for it, then explains the output column by column with a worked example you can reproduce. Updated for 2026, the screenshots-free approach below matches what SPSS 29 and later put on screen, and the syntax block works in every version back to the mid-2000s.

Table of Contents

1. 1What You Need
2. 2Step-by-Step: How to Run a Paired Samples t Test in SPSS
3. 3Check That Your Data Are Properly Paired
4. 4Open the Paired-Samples T Test Dialog
5. 5Set the Pair Number and Run the Test
6. 6Interpret the SPSS Output
7. 7Paired Samples Statistics
8. 8Paired Samples Correlations
9. 9Paired Samples Test
10. 10Check Assumptions and Report the Result in APA
11. 11Fixes for the Errors Students Hit Most
12. 12Common Mistakes and How to Fix Them
13. 13Frequently Asked Questions
14. 14How do I run a paired sample t-test?
15. 15What is the difference between a paired t test and an independent samples t test?
16. 16How do I report paired samples t test results in APA?
17. 17What does Sig. (2-tailed) mean in the Paired Samples Test table?
18. 18Why is my sample size smaller in the paired samples t test output?
19. 19Conclusion

What You Need

A paired samples t test compares the means of two related measurements on the same people, objects or occasions. You will also see it called a dependent t test, a paired t test, or a repeated measures t test in textbooks and journal articles. SPSS itself uses the name Paired-Samples T Test in every menu and every output table.

What it actually does is simpler than the menu path suggests. SPSS subtracts the second score from the first for each row, then runs a one-sample t test on those difference scores to see whether the average difference is different from zero. Every number in the output is built from that one column of differences.

You need four things before you open the dialog:

• A dependent variable measured at interval or ratio level, such as a score, a reaction time in milliseconds, a weight in kilograms, or a test result.
• Exactly two related measurements per case, stored in two separate variables. More than two conditions means a repeated measures ANOVA instead.
• One row per case in Data View, so SPSS can match the two scores that belong together.
• No extreme outliers in the difference scores, and roughly normal differences, particularly when your sample is small.

One version note worth knowing before you hunt for the menu. In SPSS 29 and later the command sits under Compare Means and Proportions. In SPSS 28 and earlier the same command is under Compare Means. Nothing else about the procedure changed, and IBM keeps documentation for both wordings.

Not sure your design is paired? This table settles it quickly.

Criterion	Paired samples t test	Independent samples t test	Wilcoxon signed-rank	Repeated measures ANOVA
What is measured	Two related scores on the same cases	Two separate groups of people	Two related scores, ranks instead of means	Three or more conditions on the same cases
Typical design	Pretest and posttest, left versus right ear, matched twins	Two different treatment groups	Small samples, skewed difference scores	Three testing occasions
Assumes normality of	The difference scores	Each group separately	Symmetry of the difference distribution	Normal residuals after removing conditions
SPSS menu	Compare Means and Proportions > Paired-Samples T Test	Compare Means and Proportions > Independent-Samples T Test	Nonparametric Tests > 2 Related Samples	Compare Means and Proportions > Repeated Measures
Use when	You have two matched score columns	Nobody appears in both groups	The normality assumption fails badly	You are testing more than one pair at once

Step-by-Step: How to Run a Paired Samples t Test in SPSS

Check That Your Data Are Properly Paired

Open your data file in Data Editor. For a paired test, the structure is one row per participant and two score columns side by side. Here is a small practice set of 20 students who completed a public speaking course, scored on a 0-40 anxiety scale before and after.

id	pre	post	pre – post
001	28	22	6
002	24	21	3
003	31	25	6
004	19	18	1
005	27	20	7

Four checks before you continue. First, each row must be one person or one matched pair, never one measurement. Second, pre and post must sit in two separate variables, not one variable with a condition column. Third, any case missing one of the two scores is dropped by SPSS, so search for empty cells in both columns and decide what to do with them. Fourth, your score variable should be coded as Scale in Variable View, not Nominal or Ordinal.

Give the identifier variable a text measurement level. If SPSS thinks id is numeric it will happily average it into a test, and you will only notice when your confidence interval comes back with a three-digit mean.

Open the Paired-Samples T Test Dialog

In SPSS 29 and later, click Analyze > Compare Means and Proportions > Paired-Samples T Test. In SPSS 28 and earlier, click Analyze > Compare Means > Paired-Samples T Test. A dialog titled Paired-Samples T Test opens with three numbered boxes: Pair 0, Pair 1 and Pair 2.

Click the empty box beside Pair 1 and drag your pre-intervention variable from the source list on the left into it. Then click the box beside Variable 2 and drag your post-intervention variable there. SPSS fills the boxes in order, so pre goes in first and post goes in second.

Two buttons in that dialog are worth a click. Options opens a panel with the confidence interval percentage and the missing-value rule, which default to 95 percent and Exclude cases analysis by analysis. Paste transfers the analysis to a syntax window instead of running it, which is the route to take if you want a reproducible record.

The Estimate effect sizes checkbox sits on the main dialog. Tick it and SPSS adds columns for Cohen’s d, Hedges’ correction, the corrected standard deviation of the difference and the average of variances. The checkbox is not there in SPSS 26 and lower, so on those versions you compute the effect size by hand.

Set the Pair Number and Run the Test

The pair number tells SPSS how many separate comparisons this run contains. One pair is one t test, and SPSS matches observations by row position rather than by any ID column. Put a fourth variable into the Variable 1 box and SPSS opens a Pair 2 slot and runs two tests in one go, which inflates the family-wise error rate unless you correct for it.

To see what SPSS built for you, click Paste and read the syntax window. For the example above it produces something close to this:

T-TEST PAIRS=pre WITH post (PAIRED)
  /CRITERIA=CI(.9500)
  /MISSING=ANALYSIS.

Run it from the syntax window with the green triangle and you get the identical result to the menu route, which is the point: a single line you can paste into any later analysis or hand to a supervisor. When you are finished, return to the dialog and click OK.

The Output Viewer opens with three tables stacked in a Notes window: Paired Samples Statistics, Paired Samples Correlations and Paired Samples Test. You can export the whole thing to Word or Excel from File > Export, and the tables stay editable text rather than images.

Interpret the SPSS Output

Here is what those tables look like for the 20-student example, with pre as Variable 1. The numbers are worked values, not a screenshot.

Paired Samples Statistics

	Mean	N	Std. Deviation	Std. Error Mean
pre	24.50	20	6.02	1.35
post	19.60	20	5.20	1.16

The standard error is the standard deviation divided by the square root of N, so 6.02 divided by about 4.47 gives 1.35. Nothing here is tested yet; these are descriptive statistics.

Paired Samples Correlations

	N	Correlation	Sig.
pre & post	20	.552	.012

This table exists because SPSS reports the relationship between the two variables as context. A strong positive correlation means the students who started anxious stayed anxious, which is common. The cell goes blank, marked as undefined, when one variable has zero variance or when there are fewer than three complete pairs.

Paired Samples Test

	Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the Difference Lower	Upper	t	df	Sig. (2-tailed)
pre – post	4.90	2.20	0.49	3.87	5.93	9.96	19	.000

Read the row from the right and the logic stays clean. The degrees of freedom are 19, which is always the number of complete pairs minus one. The absolute t value of 9.96 is the mean difference divided by the standard error of the difference, and Sig. (2-tailed) of .000 is SPSS saying the probability of a difference this large under the null is smaller than .0005, so you report it as p < .001. The 95% confidence interval of the difference runs from 3.87 to 5.93, and it excludes zero, which is the same finding in a form you can quote in a sentence.

Two things to note. SPSS never reports a real zero, so a .000 in the Sig. column means p < .001 rather than p = 0. And the row label tells you the direction: pre – post of 4.90 means scores dropped by 4.90 points on average.

Check Assumptions and Report the Result in APA

The normality assumption applies to the difference scores, not to pre and post separately. Compute the differences, then run them through Explore:

COMPUTE diff = pre - post.
EXECUTE.
EXAMINE VARIABLES=diff
  /PLOT NPPLOT
  /STATISTICS DESCRIPTIVES
  /CINTERVAL 95.

The Tests of Normality table gives a Shapiro-Wilk statistic. With 20 cases you can eyeball the Normal Q-Q plot instead: points close to the straight line mean the assumption holds comfortably. Normality matters most below about 25 pairs; with a larger sample the test tolerates moderate departures, though severe skew or a single extreme outlier can still distort the result.

Cohen’s d for a paired design is the mean difference divided by the standard deviation of the differences: 4.90 divided by 2.20, giving d = 2.23. Tick Estimate effect sizes and SPSS will report that figure alongside the average-of-variances version, which divides by a different standard deviation and so gives a smaller d of roughly 0.87 for the same data. Whichever one you report, name the formula in your methods so the number is reproducible.

The reporting template fills in directly from the output:

A paired samples t test showed that anxiety scores decreased significantly from pre-course (M = 24.50, SD = 6.02) to post-course (M = 19.60, SD = 5.20), t(19) = 9.96, p < .001, d = 2.23, 95% CI for the mean difference [3.87, 5.93].

Drop the decimals to two places for everything except the p value, never the other way round. Do not write p = .000.

Fixes for the Errors Students Hit Most

Running Independent-Samples T Test by mistake is the single most common fault. It ignores the pairing, throws away the variance reduction that makes the test sensitive, and frequently returns a non-significant result on data that are plainly different. A quick check: if your design has one row per person with two score columns, you need the paired test.

A negative t value usually means the variables sit in the boxes in the opposite order from the one you expected, not that anything failed. SPSS computes Variable 1 minus Variable 2, so swapping the two flips the sign of the mean difference and the sign of t. Report whichever direction matches your hypothesis and describe it in words, such as scores decreased from pre-course to post-course.

If your sample size in the output is smaller than the number of participants you recruited, SPSS dropped cases where either variable was missing. That is the expected behaviour of Exclude cases analysis by analysis, and you should report the analysed N rather than the recruited N.

A non-significant result is a finding, not a failure. Report the means, the confidence interval and the effect size, and say the difference was not statistically significant. If the interval is wide and sits near zero, the honest conclusion is that the study was underpowered, not that the intervention did nothing.

Common Mistakes and How to Fix Them

Almost every support thread I have read on this procedure comes back to one of the same five problems. Here they are with the fix attached.

What you see	What it means	Fix
No menu called Compare Means and Proportions	You are on SPSS 28 or earlier, or on a student licence with a trimmed menu	Use Analyze > Compare Means > Paired-Samples T Test
Pair boxes stay empty when you drag	The variables are numeric but marked as a different type, or the file is read-only	Check Variable View measurement level, then close and reopen the file
Output N is lower than either column N	Cases missing one score were dropped pairwise	Expected behaviour; report the analysed N
Correlation cell is blank or .000	Too few complete pairs, or one variable has no variation	Check the differences column for constant values
t is the wrong sign	Variable 1 and Variable 2 are in reverse order	Swap the boxes or state the direction in words

A few habits keep the whole process clean. Name your variables something readable such as pre and post rather than VAR00001, because the output labels follow your variable names. Declare missing values explicitly in Variable View instead of leaving a stray 999 in the data, since SPSS will otherwise treat that 999 as a real score. Save your syntax rather than relying on menus, so you can rerun the analysis on new data in one click.

And sanity-check against something small before you trust a real file. Run the test on the five rows in the table above, confirm the difference column matches what you subtracted by hand, and only then move to the full dataset. Forum users on r/spss regularly catch mistakes that way, by retyping a handful of rows instead of reading a screen of numbers they cannot interpret.

Frequently Asked Questions

Conclusion

Start by opening your file and confirming that one row is one person with two score columns, because every problem downstream traces back to that. From there, the menu path is fixed: Analyze > Compare Means and Proportions > Paired-Samples T Test, or Compare Means on older versions.

Then read the output as a whole rather than hunting for the p value. The means, the standard deviation of the differences, the confidence interval, the t and df, and the effect size all belong in the same sentence, and any one of them on its own will get a results section marked down.

If you take one habit from this guide, make it pasting the syntax. A single reproducible line is faster than six menu clicks and it is the thing you will still have working next year.