jamovi

Putting this really simply, I will have data from two different groups of participants where they rank order the "moral worth" of a set of non-human animals. I want to be able to test whether the rank ordering between the two groups is significantly different.

Is there an appropriate test in Jamovi for this sort of analysis? A cursory look at different ways to test this leads me to State-trace analysis which might be too in-depth for what I need (or maybe that is exactly right, just wanted to check if there was a simpler approach).

Hi, @Whirly123.
I think a simpler approach than STA is to try the Friedman test you find in jamovi [Analysis-> ANOVA-> Repeated Measures ANOVA (Non-parametric)].

Regards,
Maurizio


Update
Maybe Jonathon can say more for STA.
Here's something of hers.
https://www.researchgate.net/publicatio ... e_analysis

That doesn't seem quite right (unless I doing it wrong).

My data could look like this: And Friedman's won't let me test between groups 1 and 2.

Whirly123 wrote:That doesn't seem quite right (unless I doing it wrong).
My data could look like this:

Hi, @ Whirly123.
With the recorded data (wide type) as in the screenshot you attached, you can't do much with the Friedman test available in jamovi, but most of all you can't check if the order of the ranks between the two groups is significantly different.
The data should be organized in long type, and aggregated on the mean of the ranks before a Friedman test with the verification of the two groups.
For simplicity, if you find it useful, I am attaching a jamovi file with fake data similar to yours and a small R script in the Rj module.
The R code will create a data frame from wide to long mode and aggregate them with the rank averages and then the friedman.test() function will do the required verification for the two groups. Cheers,
Maurizio

Suggestion:
You may be interested in the book "Statistical Methods for Ranking Data" by Mayer Alvo and Philip L.H. Yu, Springer 2014.

Hello,
I'm trying to do a non-parametric repeated measures ANOVA, but I want to add a "group" variable to compare the difference in means between two different groups.

I can't do it with a Friedman ANOVA, how can I do it?

Thank you in advance for your answers.

Hello,
I'm trying to do a non-parametric repeated measures ANOVA, but I want to add a "group" variable to compare the difference in means between two different groups.

I can't do it with a Friedman ANOVA, how can I do it?

Thank you in advance for your answers.

i don't think this is possible ... at least not with the basic stats tools we typically use.

perhaps someone else can suggest something.

I would have thought a more straightforward option would be to calculate the sum total of each person's ranking across all animals - then just use a Mann-Whitney to determine if these summed totals (or means of ranks if you'd prefer) differ across the two groups.

Or are you are trying to determine if there are differences between the two groups for each individual animal? If so, then you'd need to do Mann-Whitney for each animal, but there are issues with running analyses based on a single measure - it'd be akin to separately analysing each item from a questionnaire rather than the total score....

Millet wrote:Hello,
I'm trying to do a non-parametric repeated measures ANOVA, but I want to add a "group" variable to compare the difference in means between two different groups.

I can't do it with a Friedman ANOVA, how can I do it?

What you are suggesting could be achieved a factorial ANOVA, but this not possible with non-parametric analyses. Do you absolutely have to use non-parametrics? You could try running the Friedman's ANOVA and then a separate Mann Whitney to look for overall group differences?

I would take the following approach:

(1) Identify some standard against which each person's ranks can be compared--perhaps some natural ranking of the items on some objective dimension.

(2) Compute some sort of correlation coefficient, for each participant, describing the relationship between each participant's ranks and the standard rankings. (They could be Kendall's Tau-b coefficients, for example.)

(3) Perform some sort of non-parametric, two-group comparison of the coefficients in the Group A participants against the coefficients in the Group B participants. A good comparison would be a non-parametric bootstrap of the difference between the two medians of coefficients. Alternatively, you could do a Mann-Whitney U, which would pick up any differences between the two distributions (not just differences in central tendency).