Is a Mann-Whitney U test appropriate here?

Question

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

Answer 1

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.