When I did my dissertation, there were several soon-to-be-colleagues who were irate that I did a quantitative study on qualitative data. (I was looking at cognitive bias, actually.) I needed to reduce my qualitative data so that I could represent it quantitatively. This approach to coding is called magnitude coding. Magnitude coding is just one of the 25 first cycle coding methods that Johnny Saldaña (2013) talks about in his book, The coding manual for qualitative researchers (see pages 72-77). (I know you cannot read the cover title–this is just to give you a visual; if you want to order it, which I recommend, go to Sage Publishers, Inc.) Miles and Huberman (1994) also address this topic.
So what is magnitude coding? It is a form of coding that “consists of and adds a supplemental alphanumeric or symbolic code or sub-code to an existing coded datum…to indicate its intensity, frequency, direction, presence , or evaluative content” (Saldaña, 2013, p. 72-73). It could also indicate the absence of the characteristic of interest. Magnitude codes can be qualitative or quantitative and/or nominal. These codes enhance the description of your data.
Saldaña provides multiple examples that cover many different approaches. Magnitude codes can be words or abbreviations that suggest intensity or frequency or codes can be numbers which do the same thing. These codes can suggest direction (i.e., positive or negative, using arrows). They can also use symbols like a plus (+) or a minus (-), or other symbols indicating presence or absence of a characteristic. One important factor for evaluators to consider is that magnitude coding also suggests evaluative content, that is , did the content demonstrate merit, worth, value? (Saldaña also talks about evaluation coding; see page 119.)
Saldaña gives an example of analysis showing a summary table. Computer assisted qualitative data analysis software (CAQDAS) and Microsoft Excel can also provide summaries. He notes “that is very difficult to sidestep quantitative representation and suggestions of magnitude in any qualitative research” (Saldaña, 2013, p. 77). We use quantitative phrases all the time–most, often, extremely, frequently, seldom, few, etc. These words tend “to enhance the ‘approximate accuracy’ and texture of the prose” (Saldaña, 2013, p. 77).
Making your qualitative data quantitative is only one approach to coding, an approach that is sometimes very necessary.