Thank you to everyone for your input so far. This is a very interesting topic, and something I heard come up many times while I was working with Bond, and has been brought up by IRC staff while since I have been here.
I’m afraid I can’t add a technical solution, but I have spoken to a colleague who works with results data regularly and I can pass on her experience around capturing quantitative vs. qualitative data.
"I don’t see the purpose of ascribing a value to qualitative data. They are just fundamentally different methodologies. Part of the reason why one conducts qualitative research is to better understand constructs, beliefs, attitudes, etc. that are not easily quantifiable.
For example, we conduct focus group discussions with women to learn about the barriers they face when accessing health services. We end up with a rich qualitative dataset and we pull out common themes across the groups of women we talked to. In this case, while it’s possible to say 52% of women described XX theme, it may not make sense to describe qualitative data in these terms because the sample is not representative and the %s are therefore less important than the actual themes that emerged. Moreover, qualitative and quantitative data collection are carried out for very different reasons, so while it may be technically feasible to quantify qualitative data, this doesn’t really make sense methodologically."
In short, while it is possible to wrangle qual data into a quant measure, it is time-consuming, costly and may make the data itself meaningless. This statement suggests that the original proposal is an effective way forward.