I think depending on your subgroups and sample size, you can use a Mann Whitney U, or a chi-square, or a Fisher exact. Please give us more detail on your project, the subgroups, risk factors.

Since you are working with proportions here (proportion of the Yes answer), you should run a chi-square goodness of fit test.

You should use a chi-square

**goodness of fit** test as you don't have a 2x2 table here. This chi-square compares the proportions of your sample with a default 50/50 proportion to see if the proportions in your two groups differ significantly from an even 50/50 proportion. See these links (plz click

here and

here).

ok, that makes more sense. I ran it last night and was able to do it. However, I have another question... I'm using the same procedure to look at an association between 1 demographic (nominal) variable and a likert scale (1-6). However, I have too many cell counts below 5... I looked into the fisher's exact test but I don't have that add on. My contingency table will be 5x6 I believe.

Thanks for all the help so far!!

I think the best option for your situation might be

** Rank Biserial** correlation coefficient as it deals with correlation between ordinal and nominal variables.

I think it was a misunderstanding as think he mentioned

*association* not necessarily difference; but am not sure, maybe he has said difference in another post.

I'm using the same procedure to look at an **association** between 1 demographic (nominal) variable and a likert scale (1-6).

Btw, I too agree that chi-square or dichotomizing the 6 levels (for a Fisher) were also practical, good solutions. But once I faced a similar design, and after some trials I thought that correlation test might be the best option because it maintains a higher power (I think) than a chi-square with many cells less than 5, and also doesn't loose data by collapsing the groups.