Quote:
Originally Posted by Skyehawk
I think the varied data is not a result of qualitative or quantitative. It's how invested people are in the match. [/b]
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Sorry, I may not have made this clear. The quantitative data I got during the same matches was spot on. The variation problems were not intrinsic of qualitative data collection methods or the categories being collected, but more so on things like low visibility (seating that included power outlets was pretty bad). The metrics we use eliminate the small variability that resulted therein.
We similarly use few categories/simple categories in order to maximize time watching the match and the small amount of additional notes/other qualitative data we collect is very important. In some categories, I have had significant issues with qualitative data, especially as it relates to things like driver skill and efficiency. Especially here, in the last two years I saw that the quantitative data would often tell a much different story than the qualitative: because a robot moved more cleanly, scouts would perceive it to be more efficient at scoring points while in reality it was not.
I'm curious how you plan to analyze the qualitative data; do you have a specific process that you use? I have found that our team's z-score metrics have the ability to give a very stable basis of comparison and can are completely objective. Even this year, I am fairly confident scouts can count things like high shot numbers and by simplifying other categories such as low boiler scores to low boiler cycles, I believe that qualitative data will not hold a significant advantage.
TLDR: In my experience qualitative data has hidden fluctuations and human error. I do not expect any possible fallacy in quantitative data to make it less valuable than qualitative this year.