# dynamically-weighted surveying

There are plenty of websites that try to characterize you based off a set of responses. Some surveys come via email and ask you to tally up your own score and see how you compare to the rest of the world. Some just try and answer a simple question such as what personality type or how happy or how outdoorsy you are. They’ll give 10 questions and based off how many you answer correctly, you fall into some category. Some more sophisticated applications may weight questions by importance and mathematically calculate a percentage that represents your characterization. For simplicity sake, I guess they do the job.

But here’s another idea…
One more method of weighting questions in a survey might be based off global survey or consensus results. For example, if I was to compute a score that asked, “How much of a Yankees fan are you?” two questions might be:

1) Do you hate the Red Sox?
2) Have you been to a game this year?

If a large survey was given, possible/expected results for these questions might be:
1) 99% Yes, 1% No
2) 20% Yes, 80% No

Based off these responses for a relatively large population, we can weight how much each question should factor in to the final result. For our example, since practically everyone hates the Red Sox, responding Yes should not play a majority factor in calculation of the final characterization. But since going to a game this year is a bit more of a rarity, perhaps it should contribute a higher amount to your final score. The trick is that for binary responses, you must denote which response increases the score and which decreases (it would be smart to gear the questions so that the affirmative case is always the increaser).

Taking this a step further, a lot of times the consensus of a larger group may not be known. In that case, your answers should become dynamic inputs to the weighting algorithm. They start at 50/50 and dynamically shift based on each new, incoming response. In a sense, the sensitivities are set by each new instance of that survey. Additionally, for non-binary / categorical / multiple choice responses, it would just require a bit more careful examination of weighting constituents.
Ill hopefully have an example of this weighted implementation in a near-future post.