ANALYSE-IT 2.20 > USER GUIDE
You are viewing documentation for the old version 2.20 of Analyse-it. If you are using version 3.00 or later we recommend you go to the Friedman ANOVA
This procedure is available in both the Analyse-it Standard and the Analyse-it Method Evaluation edition
Friedman test is a non-parametric test for a difference in central location (median) between two or more paired samples.
The requirements of the test are:
- Two or more paired samples measured on an ordinal or continuous scale.
Arranging the dataset
Data in existing Excel worksheets can be used and should be arranged in a List dataset layout. The dataset must contain at least two ordinal or continuous scale variables.
When entering new data we recommend using New Dataset to create a new k variables dataset ready for data entry.
Using the test
To start the test:
- Excel 2007:
Select any cell in the range containing the dataset to analyse, then click Compare Pairs on the Analyse-it tab, then click Friedman.
Excel 97, 2000, 2002 & 2003:
Select any cell in the range containing the dataset to analyse, then click Analyse on the Analyse-it toolbar, click Compare Pairs then click Friedman.
- Tick Variables to compare.
- Click OK to run the test.
The report shows the number of observations analysed, and, if applicable, how many missing values were excluded. Summary statistics for the ranks of each sample are then shown.
The Friedman statistic and hypothesis test are shown. The p-value is the probability of rejecting the null hypothesis, that the samples have the same median, when it is in fact true. A significant p-value implies that at least two samples have different medians.
METHOD The p-value is calculated using the chi-square approximation with correction for ties (see ). When the number of observations are ≤ 25 it is recommended to lookup an exact p-value (see ).
Further reading & references
- Handbook of Parametric and Nonparametric Statistical Procedures (3rd edition)
David J. Sheskin, ISBN 1-58488-440-1 2003; 845.
- Practical Non-parametric Statistics (3rd edition)
ISBN 0-471-16068-7 1999; 369.