I'm analyzing a survey over time, last year and this year, and I've been asked to compare the proportion of the two time periods for significance, from the same survey and same question data is categorical and there are three options; just different years. I am unsure as to which test I should run. I was thinking chi-square? But I also know chi-square requires independence, I'm unsure if that assumption is satisfied. If independence (is)n't satisfied which test should I use? Thank you, in advance.
Suppose 500 randomly chosen people took the survey last year. For the target question, counts for responses $1, 2, 3,$ were $a=(166, 171, 163),$ respectively.
Similarly, suppose 600 (different) randomly chosen people took the survey this year with respective counts $b=(116, 244, 240).$
If you want to know whether the pattern of responses differed between
the two years, then you can make a contingency table
DTA of the counts, and do a chi-squared test. Results from R:
a b [1,] 166 116 [2,] 171 244 [3,] 163 240 chisq.test(DTA) Pearson's Chi-squared test data: DTA X-squared = 27.555, df = 2, p-value = 1.039e-06
Because the P-value is below $0.05$ the null hypothesis that responses are independent of year is rejected at the 5% level.
This is not surprising because I sampled last year's data with equally likely responses 1, 2, 3; but this year's data with the respective proportions: 0.2, 0.4, 0.4.
Note: Complete R code for my fake data and analysis is shown below.
set,seed(724) # for reproducibility last = sample(1:3, 500, rep=T) a = tabulate(last) this = sample(1:3, 600, rep=T, pr=c(.2,.4,.4)) b = tabulate(this) DTA = cbind(a,b) chisq.test(DTA)