Codes and Coding

Saldana:

A code means a word or a short phrase that assigns a summative attributes of a certain parts of qualitative data. The first cycle of the coding process can be larger-grained than future rounds. Coding is not a precise science; it is rather an interpretive endeavor. This is different from my interpretation of coding. For me, the coding method introduced here focused more on summarizing the data rather than categorizing them and assigning nominal codes. Another difference between my research endeavor and coding methods introduced here is simultaneous coding. We quantifying qualitative data and count the frequencies. Therefore, we don’t use embedded codes a lot. That would cause trouble when we count frequencies.

There is one similarity between our ways of coding and what qualitative researchers would do. Coding is a process of judgment and assigning researchers’ perceptions to data. It is a process of re-interpretation. However, if we approach this from the perspective of active roles researchers should take, getting more involved with the research process provides more insights of the data. Therefore, coding is not a labling process, but it is a linking process. I like one metaphor in the article that coding generates the bones of the analysis, while integration of codes connects bones together. One thing that what qualitative researchers did shared with my research endeavor is to get contents and categories refined each round of coding. What we will do for refining the codes was to do some pilot coding with the codes and try some data.

Another prominent difference from our research endeavor is the theory induction. We used coding scheme by adopting from existing or adapting them from existing theoretical framework. This is more of a top-bottom method. While the method introduced in this article is more of a bottom to top one.

I totally agree with the authors’ points that doing some manual coding first before moving to electronic devices. Otherwise, our energies would waste on getting to know how to use computer tools.  

Konopasek:

The major argument of this paper is that computer tools could do more than representing what was happening in researchers’ minds. They are not replicating what researchers did pre-technology. They externalized researchers’ thinking and trying to make qualitative research explicit. The notion, which suggested qualitative research is implicit and resided in researchers’ minds, highlighted features of qualitative researches being implicit, hard to teach. It posits qualitative research as practices that are about “reading the data”. The author’s major goal, in this article, is to explain how computer tools could change the impression of qualitative research as implicit and conducting within minds of researchers, which are invisible.

I am ok with his/her points of externalizing the process of qualitative research, which is good for me, a novice in qualitative research. However, I am wondering would over-replying on coded data, in Konopasek’s words, trying to separate raw data with coded data, cause the issue of abstraction and over-generalization, which goes against to features of qualitative study, a practice embedded in rich context. 

I was keeping making connections with our earlier discussion about roles of technological tools.

Making qualitative research method explicit and accessible to new researchers sounds like to link back to the questions we discussed at the beginning of this course. This would equate qualitative research as code and retrieve, which in turn de-emphasized the role of other qualitative research methods. Moreover, I am not sure if Konopasek only used Atlas.ti as one example to illuminate his points, but his strong arguments in the article that Atlas.ti could do more than extending researchers’ minds