I work on a couple of library websites and we want to know who are users are so we can make decisions about how to design the site and focus efforts.
These are the classes of users we are interested in:
"Please select the answer that best describes yourself:"
- other [user can specify or leave blank]
We want to know the proportion of our users who fit into each category, and we want to have some sense of how accurate our numbers are. We also want to know if/how these proportions change during different times of year.
My first question is "is this a problem where I can use Bayesian stats?"
We ran a pop-up survey that went on every page of both our websites for two weeks about 6 months ago. It used a flash cookie to mark when the survey had been taken so the users would only get this once. This got logged into google analytics so we could see the % of use of the sites from each type of user. However, our websites are used a lot in library computer labs where they are set up with flash on a read only filesystem which ended up with the survey showing up on every single page since we could not really write to the flash shared object.
Can I use the data from the "shotgun" pop-up survey as my priors; and then prospectively run the pop-up survey for one out of every 1,000 unique visitors to each site? I'd have 6 to 8 new survey results coming in a day that I'd like to use to continually update a Bayesian model of who our users are.
The thing is, I have not had a stats class in 20 years and then it was classical stats. I've been googling around about discrete random variables and reading http://www.greenteapress.com/thinkbayes/ but I really have no idea what I'm talking about. Am I on the right track with this idea (if so, any pointers how to proceed? I do work in a library so I can look up any reference you can point me at) or does this sound like hogwash/gibberish?
Here is the data from the survey we ran in December 2011
Website A: 65 k-12 teacher or librarian 71 k-12 student 532 college or graduate student (we want to split this going forward) 307 faculty or academic researcher 369 archivist or librarian 234 genealogist or family researcher 584 other Website B: 133 k-12 teacher or librarian 280 k-12 student 445 college or graduate student 82 faculty or academic researcher 55 archivist or librarian 68 genealogist or family researcher 220 other