There are two issues: the technology and the workflow.
First, code your survey into LS, and then export it as a QueXML file. You'll want the survey to have very few open-ended (text) questions, since they'll probably need to be coded by hand (the OCR for handwriting doesn't work).
Then you'll want to install QueXML, and make sure that the printer and scanner you're using work for recording the answers. If you're happy with that, you need to evaluate if the propose QueXML workflow works for your 20,000 responses.
If the technology works, you can hire someone to re-do the workflow.
When I tested this 2 years ago, I couldn't get the accuracy high enough, and the workflow drove me crazy. But if you have an IT person that can spend a few hours to a couple of days configuring this, it might work for you.
First (and easiest) step is coding up the survey and printing it out in the QueXF PDF file. If nothing else, it looks very sharp.
The other approach, which might sound crazy when you first hear it, is to "Turk" the paper survey into LS (or SM). Amazon Turk (
www.mturk.com
) is an amazing tool for doing things that humans do best, like reading handwriting and transcribing survey results. We're using it now for image tagging, although it's not yet integrated into LS, something I intend to do if I can find a client to fund the development.
Tac