17.5.8 Friedman ANOVA

1 Introduction
2 Handling Missing Values
3 Performing Friedman ANOVA

Introduction

The Friedman ANOVA is a nonparametric test that compares three or more paired groups by determining the score difference between k treatments of n blocks. It performs a nonparametric analysis of a randomized block experiment and thus provides an alternative to the one-way repeated measure ANOVA.

This test requires data to follow a balanced design, i.e. exactly one observation per treatment-block combination.

The hypothesis under test, $H_0\,\!$ , often called the null hypothesis, is that the k samples come from the same population, and this is to be tested against an alternative hypothesis $H_1\,\!$ that they come from different populations.

Handling Missing Values

Friedman ANOVA does not support data with missing values. Please trim your missing values from data and make sure the data follow balanced design before running Friedman ANOVA

Performing Friedman ANOVA

To perform a Friedman ANOVA:

Select Statistics: Nonparametric Tests: Friedman ANOVA. This opens the friedman dialog box.
Specify the Input Data.
Upon clicking OK, an analysis report sheet is generated that lists degrees of freedom, Chi-square statistics, the associated p-value, and the test conclusion.