I'm working on data from a pre-post survey: the same participants have been asked the same questions at 2 different times (so the sample are not independant). I have 19 categorical variables (Likert scale with 7 levels). For each question, I want to know if there is a significant difference between the "pre" and "post" answer. To do this, I want to compare proportions in each of the 7 categories between pre and post results.
I have two data bases (one 'pre' and one 'post') which I have merged as in the following example (I've made sure that the categorical variables have the same levels for PRE and POST):
prepost <- data.frame(ID = c(1:7),
Quest1_PRE = c('5_SomeA','1_StronglyD','3_SomeD','4_Neither','6_Agree','2_Disagree','7_StronglyA'),
Quest1_POST = c('1_StronglyD','7_StronglyA','6_Agree','7_StronglyA','3_SomeD','5_SomeA','7_StronglyA'))
I tried to perform a McNemar test:
temp <- table(prepost_S1$Quest1_PRE,prepost_S1$Quest1_POST)
mcnemar.test(temp)
> McNemar's Chi-squared test
data: temp
McNemar's chi-squared = NaN, df = 21, p-value = NA
But whatever the question, the test always return NA values. I think it is because the pivot table (temp) has very low frequencies (I only have 24 participants).
One exemple of a pivot table (I have 22 participants): > temp
1_StronglyD 2_Disagree 3_SomeD 4_Neither 5_SomeA 6_Agree 7_StronglyA
1_StronglyD 0 0 0 0 0 1 0
2_Disagree 0 0 0 0 1 0 0
3_SomeD 0 0 0 0 0 1 1
4_Neither 0 0 1 1 2 2 2
5_SomeA 0 0 0 0 1 1 2
6_Agree 0 0 0 0 0 3 2
7_StronglyA 0 0 0 0 0 1 2
I've tried aggregating the variables' levels into 5 instead of 7 ("1_Disagree", "2_SomeD", "3_Neither", "4_SomeA", "5_Agree") but it still doesn't work.
Is there an equivalent of Fisher's exact test for paired sample? I've done research but I couldn't find anything helpful.
If not, could you think of any other test that could answer my question (= Do the answers differ significantly between the pre and post survey)?
Thanks!
