[Feature Rquest][RM ANOVA]: Add Siegel-Castellan post hoc test to Friedman

[PerPalmgren]

JASP Version

0.19.3

Commit ID

No response

JASP Module

ANOVA

What analysis are you seeing the problem on?

Friedman´s text (non-parametric ANOVA)

What OS are you seeing the problem on?

Windows 11

Bug Description

When I run a Friedman´s test (non-parametric ANOVA) in JASP the posthoc analysis give strange p-values (both unadjusted and corrected) compared to other statistical softawares such as SPSS (and DATAtab). See pictures below. Is there a bug in JASP

?

Expected Behaviour

Give similar p-values in the post hoc analysis as SPSS and DATAtab

Steps to Reproduce

Here is output from DATATab (similar results as SPSS) and different from JASP

Log (if any)

No response

More Debug Information

No response

Final Checklist

• I have included a screenshot showcasing the issue, if possible.
• I have included a JASP file (zipped) or data file that causes the crash/bug, if applicable.
• I have accurately described the bug, and steps to reproduce it.

[JohnnyDoorn]

Thanks for your report @PerPalmgren - can you share the data you used here? Because I verified with the PMCMRplus package and the results in Field's SPSS book and there the results seem to be in order, so I'm curious to see where the discrepancy could lie.

[PerPalmgren]

Hi @JohnnyDoorn. I used the dataset from Mark Goss-Sampson's manual “Statistical Analysis with JASP: A Guide for Students” and I attach it here. It might be the dataset, but it would be great if we can verify this. Some students pointed this out when we were doing group work using three different software programs (JASP, SPSS, and DATAtab).

Friedman RM-ANOVA 1.csv

[JohnnyDoorn]

Thanks @PerPalmgren. I've looked into it some more, and it seems there are two ways of conducting the Conover test:

• the method used by spss/datatab, is based on a very simple formula for the standard error of the test statistic
  SE <- sqrt(k * (k + 1) / (6 * n)), with k groups and n participants
• the method used by JASP, which in turn uses the PMCMRplus package, is based on a more involved method for computing the standard error, based on the observed within-ranks (see page 16 of this paper by Conover) - the R-code for the JASP calculation starts around here.

Since I am not an expert here, I will reach out to the author of the PMCMRplus package. Maybe it would've been easier if it would have been "just" a JASP bug :p

[PerPalmgren]

@JohnnyDoorn
Ok I understand. But plotting the data (see image), I think the results from the posthoc analysis from SPSS and DATAtab makes more sense.
All the best
Per the JASP lover 😎

[PerPalmgren]

@JohnnyDoorn
Was it different in JASP before? Because the results in Goss-Sampson's JASP handbook also seem more reasonable.
Per the JASP lover