## Introduction

This article describes how to conduct a correlation test between paired samples.

## Requirements

- Two variables in your dataset that you want to test for correlation.
- You should use numeric variables as inputs. If you use categorical or ordinal variables, they will be coerced to numeric based on their values for the purposes of running the test.

## Method

1. From the toolbar menu, select **Anything > Advanced Analysis > Test > Correlation**.

2. From the object inspector on the right, select your variables from the **Variable 1** and **Variable 2** drop-down box under **Inputs**.

3. OPTIONAL: Select one of the following options for the **Type of correlation**:

- Pearson product-moment (default selection) - measures the strength and direction of a linear association between the two variables.
- Spearman rank-order - a non-parametric measure of the strength and direction of association that exists between two variables on an ordinal scale.
- Kendall's Tau-b - a nonparametric measure of the strength and direction of association that exists between two variables on an ordinal scale.

Spearman rank-order and Kendall's Tau-b are rank-based measures of association which are useful when the data does not necessarily come from a bivariate normal distribution

4. OPTIONAL: Select an **Alternative hypothesis**:

- Two-sided (selected by default)
- Correlation < 0 (one-sided test)
- Correlation > 0 (one-sided test)

5. OPTIONAL: Select one of the following **Outputs**:

- Summary (selected by default)
**-**shows a nicely formatted table of the test results. - R - the original text-based output from the
`cor.test`function.

6. OPTIONAL: Tick the **Variable names** check box to display variable names in the output instead of variable labels.

7. Tick the **More decimal places** checkbox to display numeric values in the output with 8 decimal places.

8. Click **Calculate** to run the test and generate the output.

## See Also

How to Create a Correlation Matrix

How to Create a Scatter Plot Matrix

How to Create a Table of Coefficients from a Correlation Matrix

## Comments

0 comments

Article is closed for comments.