What Statistical Test to use to determine significant difference between ordinal variables pre and post intervention I have a variable that has 4 levels low, med, high, very high. The variable is from two time points, in 2013 and 2019, assessed using the same survey on the same subject/study individuals. I'm struggling to figure out exactly how to compare these. Is there a way to compare the variables as a whole? or do I compare the individual levels eg high 2013 vs high 2019? and what test would be best?
180 subjects, and the counts were recorded at each of the 4 levels eg:
2013 cleanliness: low=20 med=40 high=90 very high=30   and
2019 cleanliness: low=10 med=80 high=70 very high=20
I was considering a chi square test, but they aren't independent.
paired T test is likely also a no go?
Please correct me if I'm wrong.

Suppose we know that 120 subjects gave the same rating in the two years,
10 gave higher ratings the 2nd year and 40 gave lower ratings the second year. Then there were 50 changes of which 40 were lower
and 10 were higher. [For an an analysis of your data, you would have to make a careful tally, not available from the information given.]
A sign test would be appropriate: The null hypothesis is no change.
If a fair coin is tossed 50 times what is the probability it will show 10 heads and 40 tails. The P-value is the probability (near $0)$ of
getting a more extreme count (from 25) in either direction by chance alone, than
was observed. So the null hypothesis of no significant change is rejected.
 sum(dbinom(0:10, 50, .5)) + sum(dbinom(40:50, 50, .5))
 [1] 2.386133e-05

In the plot below, the P-value is the sum of the heights of the bars
in the two tails beyond the vertical dotted lines.

x = 0:50;  PDF = dbinom(x, 50, .5)
plot(x, PDF, type="h", lwd=2, col="blue", main = "BINOM(50,.5)")
abline(h = 0, col="green2")
abline(v = 0, col="green2")
abline(v = c(10.5, 39.5), col="red", lty="dotted")

