Have I presented this Mann-Whitney U test appropriately? I have collected data from two populations, M (males) and F (females) through a Likert scale of their agreement to a statement X
The data is the following for females F

And for M males

As you can see it is from strongly disagree to strongly agree. For analysis this was converted to a scale from 1 to 5 and a Mann-Whitney U test was done to compare the distribution of both populations' answers.

*

*Could you tell me whether I have explained this adequately in the 'analysis' part of my paper and if I have reported the results in an appropriate format? Also is using the mean (+/- SD) OK for comparing the two groups' distribution qualitatively as I have done?

Analysis: "Likert-scale data was treated as ordinal (1-5) and subsequently analysed using the Mann-Whitney U-test when appropriate"
Results: "There was no significant difference between females’ opinion (mean Likert score: 3.06 ± 1.095) and males’ opinion (mean Likert score: 3.00 ± 1.113 ) of the importance of being asked x (U = 5813, z = 0.587, p = .5552)."


*Are the results correct? I haven't used any stats software, just an online calculator (as I have no skills in R or even SPSS). Is anyone able to check?

 A: As for the check with SPSS or R, suitable R code could be the following. Unfortunately I can only tell you a way via Wilcoxon W, not Mann-Whitney U. The tests are equivalent, though:
library(exactRankTests)
f <- c(rep(1,21), rep(2,17), rep(3, 82), rep(4,34), rep(5,18))
m <- c(rep(1,7), rep(2,15), rep(3,28), rep(4,13), rep(5,8))
wilcox.exact(f, m)

The result would be
> wilcox.exact(f, m)

    Asymptotic Wilcoxon rank sum test

data:  f and m
W = 6399, p-value = 0.5343
alternative hypothesis: true mu is not equal to 0

Where you could cite R in the literature as

R Core Team (2020). R: A language and environment
for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL
https://www.R-project.org/.

and the package exactRankTests as

Torsten Hothorn and Kurt Hornik (2019).
exactRankTests: Exact Distributions for Rank and
Permutation Tests. R package version 0.8-31.
https://CRAN.R-project.org/package=exactRankTests

As for the rest of the description, that depends a lot on personal taste, faculty etc.
I for one would be careful to call something that has been measured by only one Likert-type item as a Likert scale. Also you seem to use Likert scale data and Likert score somewhat identical. Why two different words then?
Apparently, you have interviewed 243 persons. Does it seem appropriate to use that many digits for standard deviation and p-value?
So the calculation is about right, detail in the wording has to do with personal taste.
A: I have no disagreement with @Bernhard's Answer (+1),
but I will give my own comments on this using R, especially because you have not up-voted or accepted the answer, and you still seem puzzled in some of your comments.
The Likert scores and summaries are as follows:
wom = rep(1:5, c(21,17,92,34,18))

summary(wom)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    3.00    3.00    3.06    4.00    5.00 

men = rep(1:5, c(7,15,28,15,8))

summary(men)       
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   3.027   4.000   5.000 

The two sample medians are 3.0, so I think it is better just to say that, than to try to give confidence
intervals. Giving confidence intervals for means
seems undesirable because

*

*The methods for making
those confidence intervals seem to be based on an
assumption that data are from a continuous normal
distribution, while they are actually ordinal categorical data.


*Also, I agree with the objection
that CIs for means (besides being pointless) might
confuse your readers, making them wonder what those
CIs have to do with your nonparametric test (which is
nothing at all).
Boxplots leave little doubt that the medians for men and women are both $3.$
boxplot(men, wom, col="skyblue2", pch=20)


I agree that a 2-sample Wilcoxon rank sum
test does not find a difference between the two
samples of Likert scores.
wilcox.test(men, wom)

        Wilcoxon rank sum test 
      with continuity correction

data:  men and wom
W = 6829, p-value = 0.711
alternative hypothesis: 
  true location shift is not equal to 0

Data summaries and box plots seem to show a few more
low (disagree) scores among women than among men.
However, a chi-squared test of homogeneity of
Likert scores for men and women does not reject the
null hypothesis of homogeneity.
TAB = rbind(c(21,17,92,34,18),
            c( 7,15,28,15, 8))
TAB
     [,1] [,2] [,3] [,4] [,5]
[1,]   21   17   92   34   18
[2,]    7   15   28   15    8

chisq.test(TAB)

        Pearson's Chi-squared test

data:  TAB
X-squared = 7.1942, df = 4, p-value = 0.126

I think it may be sufficient to say that both Men
and Women have median 3 Likert scores and that a
Wilcoxon rank sum test (equivalent to Mann-Whitney)
finds no significant difference in locations, with
P-value 0.71. If you feel you need to say more, then
perhaps mention the P-value 0.13 for the chi-squared
test of homogeneity.
Finally, I think it is worth mention somewhere the exact numbers of men and of women in the study (and if not obvious from
the context, the reason for such different numbers).
A: This is partly a comment on @Bruce ET's helpful answer, but the graph here won't fit in a comment -- and inviting or expecting readers to enter the data and draw it for themselves is unrealistic.
The box plot isn't wrong, as box plots go, and makes the point that the medians are the same for males and females. But box plot conventions make the display overstate the difference between males and females in distribution.
Also, the box plot does precisely what is implied to be wrong about calculating means, treat the grades or ratings Strongly agree to Strongly disagree as equally spaced points on a measured scale, here 1 2 3 4 5. This is important because the box plot display hinges on calculation of median and quartiles and (specifically here) uses 1.5 IQR in deciding where whiskers stop and whether data points are shown beyond the ends of whiskers.
Indeed, experience on Cross Validated and elsewhere shows that box plots for graded or ordinal data like these -- more generally, for data with many ties -- are often puzzling. They can even provoke suspicions that something is wrong. (Usually the software is put in question, not the reader of the graph.) These example threads understate the puzzlement box plots can cause.
Boxplot interpretation: is it correct that a boxplot is missing a whisker?
Help needed with my box plot
A plain bar chart explains why and how the box plot muddies the picture. Bar lengths here are proportional to percents given gender, but the annotation shows absolute counts too. Indeed, my bar chart also shows grades equally spaced, but nothing depends on that conventional spacing.

For males, the distribution is such that median and lower quartile agree at 3. So, the interquartile range is just 1: this is clear from the graph, as it is the height of the box. So, the lowest value 1 qualifies for separate display: it is 2 below the lower quartile, and so more than 1.5 IQR away from the lower quartile, which is the most common convention for separate display of low values and that used by R in this case. (I don't join the poor practice of shouting "outlier" here.)
For females small differences between the distributions make the lower quartile emerge as 2, and the lowest value 1 is not selected for separate display.
The box plot does not, and cannot, tell you much about the relative frequency of grades of 1, which are not much different for males and females, or about the relative frequency of any other grade for that matter.
