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I need to run a stats analysis on data at the bottom of this post. Essentially, I want to investigate whether Diet Group (categorical independent var) or # of Stitches (categorical independent var) affects Score (ordinal dependent var).

From what I understand, I would need to use a non-parametric test as my data doesn't seem to be normal / there isn't enough to convince me that I have a normal dataset. This would mean that I can't use a two-way ANOVA (which I was originally recommended) to investigate all three variables together.

From my discussions, I believe I should be conducting two Mann-Whitney U tests (one for Diet Group vs. Score and one for # of Stitches vs. Score. However, as my dependent var (Score) essentially falls mostly within two groups (either 1 or 2) even though the scoring scale itself is 0 - 4, I was informed that these ranked tests wouldn't be ideal as they don't do well when there are lots of ties within the ranking.

Are there any other statistical tests that I should be using with this non-ideal data set or is Mann-Whitney U the best I'd be able to do?



Raw Data:

animal diet group # stitches score
A ED 5 2
B ED 5 0
C ED 5 2
D ED 5 1
E ED 5 1
F ED 5 1
G ED 5 1
H ED 5 2
I ED 5 2
J WD 5 2
K WD 5 2
L WD 5 2
M WD 5 2
N WD 5 1
O ED 7 2
P WD 7 1
Q WD 7 2
R WD 7 2
S WD 7 2
T WD 7 2
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1 Answer 1

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You can correct for ties in the Mann-Whitney test, have you considered using this version? Some software, e.g., Statistica calculate this automatically.

A tie correction method can be found on Wikipedia: https://en.wikipedia.org/wiki/Mann–Whitney_U_test

Note, that it is still not ideal since your outcome/dependent variable only has three categories.

An alternative could be multinomial logistic regression but your sample size may be too small for this, especially if you originally planned for a t-test in your sample size calculation.

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    Commented Oct 10, 2021 at 6:39

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