# SPSS Correlation Analysis – Overview

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 .

# 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 Command

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