Eisner discusses 6 features of qualitative study:
- qualitative studies are field focused; that is, anything that is important for education is potential subject matter, from observing teachers in the classroom to how children play to the design of the trophy case in the school
- the researcher as instrument; we all make judgments as to what is or isn't important, and calling out one's own biases is as important as identifying those of others
- a qualitative study is interpretive; thick description, motivation, underlying attitudes, and nuanced differences are essential
- expressive language and presence of voice; this goes along with the researcher as instrument in that it is false to assert a neutrality that does not exist
- attention to particulars; rather than run details through a statistics engine to arrive at a general statement, the analysis of outliers and unique details of an individual situation, individual, event, or object provides a flavor that is often lost
- triangulation; qualitative researchers look for multiple sources to validate findings, not a sterile statistical test of significance.
I'm reminded of the story of a quantitative and a qualitative researcher observing a man mowing his lawn. The quantitative researcher measures the length of the grass before and after it is cut, in addition to the average and standard deviations of the length of the grass of his neighbors, and explains that he was cutting his grass in order to keep it within a certain threshold acceptable for that street. The qualitative researcher asks the man why he is cutting his lawn, to which he replies he is doing it early in the morning to get back at the neighbors who were up late loudly partying the night before.
Of course, both quantitative and qualitative are important, and you can't really have one without the other. Straight quantitative research loses touch with reality fairly quickly, and qualitative research simply isn't scalable. Problems can be identified by either method or a combination of the two. Heavy quantitative analysis is often used to develop and validate interventions that work for the majority, but then qualitative work is necessary to really understand whether the desired effects are taking hold and to work with those who don't fit in the majority mold.