If all your questions have the same response scale and they are standard Likert items, scaling the item 1,2,3,4,5 and taking the mean is generally fine.
You can investigate the robustness of the rank ordering by experimenting with different scaling procedures (e.g., 0, 0, 0, 1, 1 is common where you want to assess the percentage happy or very happy; or agreeing or strongly agreeing). From my experience, such variants in scaling will give you almost identical question orderings. You could also explore optimal scaling principal components or some form of polytomous IRT approach is you wanted to be sophisticated.
A table with three columns would be fine: rank, item text, mean. You could also do the same thing with question on the x axis and mean on the y axis.
I agree with @rolando2's suggestion that Spearman's/Kendall's might be better suited. In general doing this by hand is just inconvenient but if you want to have a look at this excellent Khan Academy clip that shows exactly how to do a $\chi^2$ test.
I suggest you use some software, my current favorite is R together with RStudio as your IDE
First create your dataset, preferably in a spreadsheet and then import it but you can also create the data in R:
my_question1 <- c(1, 1, 3, 1, 4, 3, 1, 4, 4, 3, 2)
my_question2 <- c(1, 2, 4, 3, 3, 5, 2, 5, 5, 4, 1)
Then the $\chi^2$ test
chisq.test(my_question1, my_question2)
If you have a cell with few outcomes (5 or less) you should use Fisher's exact test:
fisher.test(my_question1, my_question2)
For the Spearman method use:
cor.test(my_question1, my_question2, method="spearman")
And for the Kendall use:
cor.test(my_question1, my_question2, method="kendall")
Hope this helped
Best Answer
I upvoted simply because the downvote was uncalled-for.
To answer the question: