Pearson Correlation in SPSS: How to Run and Interpret


 

If you are analysing questionnaire data for your thesis, sooner or later you will need to answer a simple question: do these two variables move together? The Pearson correlation in SPSS is the standard tool for exactly that job. It tells you how strongly two continuous variables are related, in which direction, and whether the relationship is statistically significant.

In this guide you will learn what Pearson correlation measures, the assumptions you must check first, how to run it in SPSS in six steps, how to interpret the output table, and how to report the result in APA 7th edition. A full worked example is included so you can see what a real result looks like before you touch your own data.

What Is Pearson Correlation in SPSS?

Pearson correlation, written as Pearson's r, measures the strength and direction of the linear relationship between two continuous variables. It produces one number, r, which ranges from -1 to +1:

  • r = +1 means a perfect positive linear relationship. As one variable rises, the other rises too.
  • r = -1 means a perfect negative linear relationship. As one variable rises, the other falls.
  • r = 0 means no linear relationship at all.

For theses, the value is rarely at those extremes. Use Cohen's (1988) guidelines to judge the strength of your result:

r valueStrength
.10 to .29Small
.30 to .49Medium
.50 to 1.0Large

The same bands apply to negative values. Typical thesis uses include the relationship between stress scores and sleep hours, study hours and exam marks, or customer satisfaction and purchase intention. Note that r alone is not enough: you also need the p-value (Sig. 2-tailed) to decide whether the relationship is statistically significant.

Pearson Correlation Assumptions: The 4 Checks Before You Run It

Pearson's r gives misleading results when its assumptions are broken. Check these four before running the test:

1. Both variables are continuous. They should be measured at the interval or ratio level, such as scores, heights, or ages. If one variable is ordinal, run Spearman's correlation instead.

2. The relationship is linear. Draw a scatterplot of your two variables first. If the points form a straight-line pattern, Pearson is fine. If they curve, Pearson will understate the relationship.

3. No extreme outliers. Pearson's r is highly sensitive to outliers. One extreme case can inflate or hide a real correlation. Inspect the scatterplot and remove or transform genuine data errors.

4. Approximately normal distributions. Each variable should be roughly normally distributed. Check this with a histogram and the Shapiro-Wilk test (Analyze > Descriptive Statistics > Explore). With larger samples (n > 100), minor departures are tolerable.

If any check fails badly, consider Spearman's rank correlation, which does not require normality or linearity.

How to Run a Pearson Correlation in SPSS (Step by Step)

Follow these six steps in SPSS:

Step 1: Open your data file and make sure the two variables are in separate columns and coded numerically.

Step 2: Inspect a scatterplot first. Go to Graphs > Chart Builder, choose Scatter/Dot, and drag one variable to the X axis and the other to the Y axis. Look for a straight-line pattern and outliers.

Step 3: Click Analyze > Correlate > Bivariate.

Step 4: Move your two variables into the Variables box. SPSS will analyse the pair automatically.

Step 5: Under Correlation Coefficients, make sure Pearson is ticked. Under Test of Significance, select Two-tailed. Tick Flag significant correlations so SPSS marks significant results with asterisks.

Step 6: Click OK. The Correlations table appears in the Output Viewer.

That is the whole procedure. The skill is in reading what comes out.

Pearson Correlation SPSS Output: How to Interpret It

The output table has three numbers you need for each variable pair:

Pearson Correlation (r). The headline number. Read the direction from its sign and the strength from the bands above. A value of .62 means a large positive relationship.

Sig. (2-tailed). This is the p-value. If it is below .05, the correlation is statistically significant: the relationship is unlikely to be a fluke of sampling. Values like .000 mean p < .001, not exactly zero.

N. The number of cases used in the analysis. Watch this number: it tells you whether missing data cut your sample down.

Two traps to avoid here. First, a small r can be significant with a very large sample, but that does not make it important. A correlation of .12 that is significant with n = 2,000 is still a weak relationship. Second, a large r can be non-significant with a tiny sample. Always report both the size and the significance, never one alone.

Worked Example: Interpreting a Pearson Correlation SPSS Result

Suppose a researcher measured test anxiety (scored 0 to 80) and final exam score (percentage) in 150 undergraduate students. After checking the assumptions, she ran a Pearson correlation in SPSS and got this output:

Test AnxietyExam Score
Test Anxiety — Pearson Correlation1-.62**
Sig. (2-tailed).000
N150150
Exam Score — Pearson Correlation-.62**1
Sig. (2-tailed).000
N150150

Reading it step by step:

  • Direction: r = -.62 is negative. As test anxiety goes up, exam scores go down.
  • Strength: .62 is a large effect by Cohen's guidelines.
  • Significance: p < .001 (shown as .000), well below .05, so the relationship is statistically significant.
  • Shared variance: squaring r gives r squared = .38. Test anxiety accounts for roughly 38 percent of the variation in exam scores.

This is a strong, meaningful result for a thesis discussion. But note the limit: this does not prove that anxiety causes lower scores. A third factor, such as poor study habits, could drive both. Correlation never establishes causation.

How to Report Pearson Correlation in APA 7th Edition

APA 7th has strict formatting rules for correlations. Report r in italics, include degrees of freedom in parentheses (df = N - 2), and give the exact p-value where possible:

A Pearson correlation coefficient was computed to assess the relationship between test anxiety and exam performance. There was a strong negative correlation between the two variables, r(148) = -.62, p < .001.

Four rules to get right:

1. Italicise r and p, but not the numbers in parentheses. 2. Do not use a leading zero for r (write r = -.62, since a correlation cannot exceed 1). 3. Report df as N minus 2. 4. Write p < .001 instead of p = .000.

Also add a table of your correlation matrix in Chapter 4 and mention the effect size interpretation in the text.

Common Pearson Correlation Mistakes to Avoid

Mistake 1: Claiming causation. The most common error in thesis discussions. Your correlation shows two variables move together; it never shows one causes the other. Write "associated with", not "led to".

Mistake 2: Ignoring outliers. One extreme respondent can swing r dramatically. Always plot the scatterplot before interpreting.

Mistake 3: Using Pearson on single Likert items. A single 1-to-5 Likert question is ordinal, not continuous. Pearson is fine on summed scale scores (for example a total anxiety score), but for single items use Spearman.

Mistake 4: Reporting r without the p-value. The effect size and the significance test answer different questions. Readers need both, plus the sample size.

Mistake 5: Running correlation on tiny samples. With n = 20, even a moderate r will be non-significant. If your pilot sample is small, treat the correlation as exploratory.

Conclusion

Pearson correlation in SPSS is a short procedure but a long interpretation. Check the four assumptions, inspect the scatterplot, run the Bivariate Correlations dialog, then read r for strength, the p-value for significance, and N for the sample actually used. Report everything in APA 7th style with the degrees of freedom and exact p-value, and never let your discussion claim causation from a correlation.

Run through these steps on your own data today, and your Chapter 4 results section will practically write itself.

Need help with your thesis or SPSS analysis? WhatsApp Farzana at +92 326 2047345.

Struggling with SPSS for your thesis? Get the step-by-step guide SPSS Made Simple — 30-page ebook + Excel templates pack. Buy it here: https://whop.com/researchmadesimple/spss-made-simple-a-step-by-step-guide-for-psychology-social-science-students/


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