Your data should be an
ID vector indexing people, and their responses to the questions:
ID Q1 ... Q17
1 1 1
2 1 0
3 0 0
These can be organized into a contingency table:
Q1 1 32 15
0 7 26
There are two ways you could analyze these, depending on what question you want to answer. If you were thinking of these as different ways of asking the same question, and wanted to check for consistency (i.e., are people just marking the sheet randomly to get through it as quickly as possible), you could run Cohen's $\kappa$. However, I doubt that is your question. More likely, you wonder if the addition of the information about the potential presence of disabled workers changes people's opinion. The way to answer this question is using McNemar's test. I have a rather thorough explanation of McNemar's test here: What is the difference between McNemar's test and the chi-squared test, and how do you know when to use each?
Visualizing this won't show too much, because you only have a 2x2 table. Just presenting the table is probably enough. If you wanted, you could make a mosaic plot. Here is an example of such a mosaic plot (copied from my question, Alternative to sieve / mosaic plots for contingency tables, which includes some other possibilities as well):
If you use
R, the above could be performed thusly:
tab = with(my.data, table(Q1, Q17))
See: ?table, ?kappa, ?mcnemar.test, and ?mosaicplot.