# Friedman Test in SPSS Statistics

## Introduction

The Friedman test is the non-parametric alternative to the one-way ANOVA with repeated measures. It is used to test for differences between groups when the dependent variable being measured is ordinal. It can also be used for continuous data that has violated the assumptions necessary to run the one-way ANOVA with repeated measures (e.g., data that has marked deviations from normality).

###### SPSS Statistics

## Assumptions

When you choose to analyse your data using a Friedman test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a Friedman test. You need to do this because it is only appropriate to use a Friedman test if your data "passes" the following four assumptions:

**Assumption #1:****One group**that is measured on**three or more different occasions**.**Assumption #2:**Group is a random sample from the population.**Assumption #3:**Your**dependent variable**should be measured at the**ordinal**or**continuous level**. Examples of**ordinal variables**include Likert scales (e.g., a 7-point scale from strongly agree through to strongly disagree), amongst other ways of ranking categories (e.g., a 5-point scale explaining how much a customer liked a product, ranging from "Not very much" to "Yes, a lot"). Examples of**continuous variables**include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. You can learn more about ordinal and continuous variables in our article: Types of Variable.**Assumption #4:**Samples do**NOT need to be normally distributed**.

The Friedman test procedure in SPSS Statistics will not test any of the assumptions that are required for this test. In most cases, this is because the assumptions are a methodological or study design issue, and not what SPSS Statistics is designed for. In the case of assessing the types of variable you are using, SPSS Statistics will not provide you with any errors if you incorrectly label your variables as nominal.

###### SPSS Statistics

## Example

A researcher wants to examine whether music has an effect on the perceived psychological effort required to perform an exercise session. The dependent variable is "perceived effort to perform exercise" and the independent variable is "music type", which consists of three groups: "no music", "classical music" and "dance music". To test whether music has an effect on the perceived psychological effort required to perform an exercise session, the researcher recruited 12 runners who each ran three times on a treadmill for 30 minutes. For consistency, the treadmill speed was the same for all three runs. In a random order, each subject ran: (a) listening to no music at all; (b) listening to classical music; and (c) listening to dance music. At the end of each run, subjects were asked to record how hard the running session felt on a scale of 1 to 10, with 1 being easy and 10 extremely hard. A Friedman test was then carried out to see if there were differences in perceived effort based on music type.

###### SPSS Statistics

## Setup in SPSS Statistics

SPSS Statistics puts all repeated measures data on the same row in its Data View. Therefore, you will need as many variables as you have related groups. In our example, we need three variables, which we have labelled "none", "classical" and "dance" to represent the subjects' perceived effort when running based on the three different types of music.

Published with written permission from SPSS Statistics, IBM Corporation.

If you are still unsure how to enter your data correctly, we show you how to do this in our enhanced Friedman test guide. You can learn about our enhanced data setup content on our Features: **Data Setup** page, or our enhanced guides as a whole on our Features: **Overview** page.