SPSS Correlation Analysis – Overview

Pearson Correlations

In statistics we find several different correlations but simply “correlation” usually refers to the Pearson correlation: a number between -1 and 1 that indicates how strongly two metric variables are linearly related.
A second correlation, the Spearman correlation, can be used for ordinal variables. In SPSS, both correlations are found under Correlate SPSS Menu Arrow Bivariate.

SPSS Correlation - Menu

SPSS Correlation Tutorials

Correlation Coefficient – What Is It?

Correlation coefficients explained in normal language with illustrations and examples. Read More

SPSS Correlation Test – Simple Tutorial

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” ... (Read More) Read More


SPSS CORRELATIONS generates tables with Pearson correlations and their underlying N’s and p-values. For other correlation coefficients such as Spearman’s, try the NONPAR CORR command. Read More

Cramér’s V – What and Why?

Cramér’s V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It’s often used together with a chi-square independence test. Read More

SPSS Correlation Tools

SPSS Confidence Intervals for Correlations Tool

This tutorial presents a freely downloadable, user friendly tool for computing confidence intervals for Pearson correlations in SPSS. With easy-to-follow examples and instructions. Read More

SPSS Scatterplot Tutorial

What are scatterplots, why are they useful and how can you create them fast in SPSS? This tutorial walks you through quickly. Read More

SPSS Correlations without Significance

SPSS CORRELATIONS generates tables containing correlations, the N for each correlation and its supposed significance. Unfortunately, there's no simple way to suppress the latter two pieces of information. This tutorial therefore proposes a simple tool for hiding the significance and/or N after the correlation tables have been produced. Read More