# 17.1.11 Correlation Coefficient (Pro Only)

## Introduction

The correlation coefficient is a numerical measure of the strength of the relationship between two random variables. The value of the correlation coefficient varies from -1 to 1. A positive value means that the two variables under consideration have a positive linear relationship (i.e., an increase in one corresponds to an increase in the other) and are said to be positively correlated. A negative value indicates that the variables considered have a negative linear relationship (i.e., an increase in one corresponds to a decrease in the other) and are said to be negatively correlated. The closer the value is to +1 or -1, the stronger the degree of linear dependence. ## Choosing a Correlation Test

 Pearson's r Correlation This widely-used coefficient measures the strength of a linear association between variables. Spearman's Rank Order Correlation The most common non-parametric measure, Spearman's is used when data are not normally distributed. Spearman's is a non-parametric equivalent of Pearson's correlation. Kendall's tau Correlation Another non-parametric method, used when analyzing data with one or more ordinal variables. Kendall’s is relatively "robust" to outliers. ## Handling Missing Values

When there are missing values in your data, the Correlation Coefficient dialog provides the option to delete the cases pairwise or listwise.

## Performing Correlation Coefficients

To open the Correlation Coefficients dialog box from the menu:

• Select Statistics: Descriptive Statistics: Correlation Coefficient...
 Topics covered in this section: Tutorial