SPSS correlation test is a procedure for testing whether two metric variables are linearly related in some population. The extent to which they are is usually expressed by a number, called the correlation coefficient.
There are a number of different correlation coefficients but “correlation” usually means product moment correlation coefficient, better known as “**Pearson correlation**” (unless otherwise specified).
The null hypothesis implies that no linear relation whatsoever is present in between the variables, which implies a correlation of 0. The figure below illustrates the basic idea.

## SPSS Correlation Test Example

A policy maker wants to know whether age and nett monthly income are related in any way. She asks 30 respondents, resulting in age_income.sav. Do these data render it likely that age and income are related in the research population? The syntax below opens the data.

***1. Set default directory.**

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

***2. Open data.**

get file 'age_income.sav'.

## 1. Quick Data Check

Before running any statistical tests, we first want to have an idea of what the data basically look like. A nice option here is a scatter plot. The screenshots walk you through running one.

We'll first navigate to

Next, we select

click

We move `income`

to and

`age`

to .

Clicking results in the syntax below.

***Run scatter plot of age versus income.**

GRAPH/scatter age with income.

## Resulting Scatter Plot

In this case, the scatter plot looks plausible. All respondents have an `age`

between, say, 20 and 68. The ages are reasonably spread out with an average around 45. Next, `income`

ranges from roughly €1000,- through €4500,-. This is the kind of range we'd expect for monthly income in a developed country.

On top of how `age`

and income `income`

are distributed separately, we also see that older respondents tend to have higher incomes. This indicates a positive correlation between age and income.

## 2. Assumptions Correlation Test

Interpretation of the correlation coefficient itself doesn't require any assumptions. However, the significance test for a correlation does make some basic assumptions. These are

- independent observations (or, more precisely, independent and identically distributed variables);
- the sample size is reasonably large (say N > 30);

## 3. Run SPSS Correlation Test

The screenshot shows the standard way to obtain correlations. However, this produces messy syntax and output so we'll do it differently; we could just type and run
correlations age income.
We think this is a faster and cleaner way to obtain a full correlation matrix.Note that you can use the TO and ALL keywords in this command if you have multiple variables. A better alternative, resulting in cleaner output, is using the `WITH`

keyword as shown in the syntax below.Insofar as we know, this clean output cannot be obtained from the menu.

***1. Full correlation matrix.**

correlations age income.

***2. Custom correlation matrix.**

correlations age with income.

## 4. SPSS Correlation Test Output

The correlation itself is .730. This indicates a strong (positive) linear relationship between age and income;

The p-value, denoted by “Sig. (2-tailed)”, is .000. If the correlation is 0 in the population, then there's a 0% chance of finding the correlation we found in our sample. The null hypothesis is often rejected if p < .05. We conclude that the correlation is not 0 in the population (we now expect it to be somewhere near .73).

More precisely, since this is a **2-tailed test**, the p-value consists of a 0% chance that the sample correlation is larger than .73 and another 0% chance that it's smaller than -.73.

The results are based on N = 30 cases. Since this corresponds to our sample size, we conclude that there are no missing values in our data.

## 5. Reporting a Correlation Test

When reporting a correlation test, the correlation itself and the N on which it's based are mandatory. Regarding the significance test, a short way of reporting it is “A strong linear relation was observed between age and income, Pearson correlation = .73, p = .000 (2-sided).” Now, there are multiple ways of calculating a p value for a correlation. By reporting like we just did, it's not obvious which method was used. SPSS uses a t-test here but -unfortunately- omits the t-value and degrees of freedom.The formulas that SPSS uses here are found under We built an .SPSS custom dialog for obtaining them, which can be freely downloaded from t-test for Pearson correlation tool.

In our case, it prints the following to our output viewer:

We can now make clear how we arrived at our p value by reporting ** “We found a Pearson correlation of .73; t(28) = 5.7, p = .000 (2-sided)”**.

## This tutorial has 30 comments

## By Ruben Geert van den Berg on December 5th, 2016

Hi James, thanks for your comment! Which way to go really depends on your research questions and the structure of your data. I can't tell without looking into those in detail.

P.s. there's some new tutorials coming up regarding alternative correlation measures such as CramÃ©r's V, Kendall's Tau, Spearman and others. Follow us on facebook for staying up to date with our latest articles.

## By JAMES MUNGAI MUCHIRI on December 5th, 2016

i have staff and student data regarding how they rate a particular course is it possible to do some correlation test here.

## By J. Walsh on November 19th, 2016

Enlightening! Thank you very much!

## By rebecca on October 31st, 2016

great i got to understand it better with your explanation

## By Ruben Geert van den Berg on September 19th, 2016

Thanks for the compliment! Please tell everybody about it.