If you’re working with survey results, test scores, sales numbers, or any dataset with numeric variables, correlation is often the first analysis you run. It helps you understand whether two variables move together and how strongly they’re related. This guide walks you through how to do correlation in SPSS step by step, using the most common method: bivariate correlations (especially Pearson’s correlation). You’ll also learn how to interpret correlation matrix outputs, and how to read a pearson r table, pearson correlation coefficient table, and pearson correlation table without confusion.
What correlation means
Correlation measures the strength and direction of a relationship between two variables:
Direction
Positive correlation: as X increases, Y tends to increase.
Negative correlation: as X increases, Y tends to decrease.
Strength
Values closer to +1 or -1 indicate a stronger relationship.
Values near 0 suggest little to no linear relationship.
The most common statistic you’ll see in SPSS is Pearson’s r (the Pearson correlation coefficient).
Before you start in SPSS: check your data
To get reliable results, do these quick checks:
Variable type
Pearson correlation is best for scale/continuous variables (interval/ratio).
2. Missing values
SPSS can exclude missing values pairwise or listwise; your choice affects results.
3. Outliers
Extreme values can inflate or hide correlations.
4. Linearity
Pearson’s r captures linear relationships. If the relationship is curved, r may be low even if variables are clearly related.
If your data is ordinal (ranked) or not normally distributed, you may prefer Spearman’s rho. But since most users want Pearson in SPSS, we’ll focus there.
How to do correlation in SPSS (step-by-step)
Here’s the exact path in SPSS to run correlations:
Step 1: Open your dataset
Go to File > Open > Data and select your file (e.g., .sav, .xlsx, .csv).
Step 2: Go to Bivariate Correlations
Click Analyze > Correlate > Bivariate…
This is the standard workflow for bivariate correlations in SPSS.
Step 3: Choose variables
In the left panel, select the variables you want to correlate.
Click the arrow to move them into the Variables box.
Tip: You can select more than two variables. SPSS will output a full correlation matrix.
Step 4: Select correlation type
In the correlation coefficients section, choose:
✅ Pearson (most common)
Optional:Spearman (rank-based)
Kendall’s tau-b (often for small samples/ordinal data)
Step 5: Choose significance options
Test of Significance
Two-tailed is most common (checks for any relationship, positive or negative).
One-tailed only if you have a strong directional hypothesis.
Tick Flag significant correlations so SPSS marks significant results with asterisks (*).
Step 6: Handle missing data
Under “Missing Values,” choose:
Pairwise: uses all available data for each pair (more data retained, but N can vary).
Listwise: uses only cases with no missing values across selected variables (consistent N, but may reduce sample).
Step 7: Run the analysis
Click OK.
SPSS will output a correlation table (often called a pearson correlation table or pearson r table) in the Output Viewer.
Understanding the SPSS output: correlation matrix explained
When you include multiple variables, SPSS gives a correlation matrix—a table showing correlations for every pair of variables.
This section is the core of how to interpret correlation matrix results.
What you’ll see in the correlation matrix
For each variable pair, SPSS usually reports:
Pearson Correlation
This is the r value (ranges from -1 to +1).
Sig. (2-tailed)
This is the p-value. It tells you whether the correlation is statistically significant.
N
The number of cases used for that correlation.
So, the pearson correlation coefficient table in SPSS is basically telling you:
“How strong is the relationship (r), is it significant (p), and how many data points were used (N)?”