By Ruben Geert van den Berg on September 16, 2014 under SPSS T-Test Tutorials.

# SPSS Paired Samples T Test

SPSS paired samples t-test is a procedure for testing whether the means of two metric variables are equal in some population. Both variables have been measured on the same cases. Although “paired samples” suggests that multiple samples are involved, there's really only one sample and two variables. The screenshot below illustrates the basic idea.

## SPSS Paired Samples T-Test Example

A behavioral scientist wants to know whether drinking a single glass of beer affects reaction times. She has 30 participants perform some tasks before and after having a beer and records their reaction times. For each participant she calculates the average reaction time over tasks both before and after the beer, resulting in reaction_times.sav. Can we conclude from these data that a single beer affects reaction time? We'll first open the data by running the syntax below.

***1. Set default directory.**

cd 'd:downloaded'. /*or wherever data file is located.

***2. Open data.**

get file "reaction_times.sav".

## Quick Data Check

We first just want to know what the data look like. We could do so by taking a look at the histograms of the two variables. However, a nice alternative for two variables measured on the same respondents is a scatter plot. The screenshots below walk you through.

We first navigate to

We then move `reac_1`

and `reac_2`

to and . Clicking results in the syntax below.

***Run scatter plot.**

GRAPH

/SCATTERPLOT(BIVAR)=reac_2 WITH reac_1

/MISSING=LISTWISE.

Normal reactions times are between 800 and 1500 ms (= milliseconds). Neither variable has any values that are way out of this normal range so the data seem plausible.

We also see a substantial positive correlation between the variables; respondents who were fast on the first task tend to be fast on the second task as well. The graph seems to suggest that the mean reaction time before a beer is somewhere near 1100 ms (vertical axis) and after a beer perhaps 1300 ms (horizontal axis).

One respondent (right top corner, denoted “outlier”) is remarkably slow compared to the others. However, we decide that its scores are not extreme enough to justify removing it from the data.

## 2. Assumptions Paired Samples T-Test

SPSS will happily provide us with test results but we can only take those seriously insofar as the assumptions for our test are met. For the paired samples t-test, these are

**independent observations**or, more precisely, independent and identically distributed variables;- the difference scores between the two variables must be
**normally distributed**in our population.

The first assumption is often satisfied if each case (row of data values) holds a distinct person or other unit of analysis. The normality assumption is mostly relevant for small sample sizes (say N < 30). If it's violated, consider a Wilcoxon signed-ranks test instead of a t-test. However, our data seems to meet both assumptions so we'll proceed to the t-test.

## 3. Run SPSS Paired Samples T-Test

We'll first navigate to

.
Select both variables and

move them into the box.

Clicking results in the syntax below.

***Run paired samples t-test.**

T-TEST PAIRS=reac_1 WITH reac_2 (PAIRED)

/CRITERIA=CI(.9500)

/MISSING=ANALYSIS.

## 4. SPSS Paired Samples T-Test Output

The first table (“**Paired Samples Statistics**”) presents the descriptive statistics we'll report. (Do not use the `DESCRIPTIVES`

command for obtaining these.The reason for this is that the significance test is (necessarily) limited to cases without any missing values on the test variables. Cases with a missing value on one of the test variables should therefore be excluded from the descriptives as well. This holds for the descriptives from the paired samples t-test but not for `DESCRIPTIVES`

.)

Since N = 30, we don't have any missing values on the test variables and

as expected, the mean reaction time before a beer (1166 ms) is lower than after a beer (1288 ms).

On average, respondents slow down some 122 ms. We could have calculated this from the first table ourselves.

The p-value denoted by “Sig. (2-tailed)” is 0. (If we double-click it, we'll see it's precisely 0.000083, meaning a .0083 % chance.) So if the population means are equal, there's a 0% chance of finding this result. We therefore reject the null hypothesis. Even a single beer slows people down on the given tasks.

Note that the p-value is **two-sided**. This means that the p-value consists of a .00415% chance of finding a difference < -122 ms and another .00415% chance of finding a difference > 122 ms.

## 5.Reporting a Paired Samples T-Test

As we mentioned before, we'll always report the descriptive statistics obtained from the paired samples t-test. For the significance test, we may write something like ** “Participants became slower after drinking a single beer, t(29) = -4.6, p = 0.00”**.

## This Tutorial has 7 Comments

## By Adebayo on September 28th, 2016

HI, AM VERY NEW TO SPSS, AND I NEED IT FOR MY FINAL PROJECT REPORT. I NEED TO KNOW WHICH TEST EXACTLY TO USE TO ANALYZE THE RESULTS OF DIFFERENT OBSERVATIONS (7 OBSERVATIONS) OF 2 DIFFERENT SAMPLES.

## By Maryam on April 20th, 2016

Hi again.

I get my answer of prior question as the other user asked the same.

Now, I cannot understand why one of my subscales is significant in paired T test, but not in repeated measure!?

Thanks

## By Maryam on April 20th, 2016

Hi,

Would you please guide me?

I used one questionnaire with 7 sub-scales for pre-post experiment.

Could I use repeated measure for this plan?

2) paired T test can be used for my plan? with 7 subscales?

Thanks

## By Ruben Geert van den Berg on February 1st, 2016

The most likely option, indeed, is to examine each pair separately with a paired samples t test. In this case you probably want to consider a Bonferroni correction for the p-values. This approach will identify which pairs show different means and in which directions. I'd go with this approach. Oh, and I'd certainly visualize the means of the variable pairs for some real insight.

Alternatively, you could analyze the entire set by using a repeated measures ANOVA with two within-subjects factors: type of test and occasion. In this case, effects of variable pairs may cancel each other out so you may not demonstrate effects that do exist. Reversely, if you do demonstrate any effects, you'll probably want to throw in some post-hoc tests which are very similar to paired samples t tests for identifying which pairs of variables have different means. I generally wouldn't prefer this option.

## By Aideen Marie Osborne on January 31st, 2016

If I have multiple variables but all paired do I run each pair through the paired t-test individually or I can have several lines analysed at once?

Thanks Aideen