Friday, November 23, 2007

NVivo7 Test Drive Report #4

Preliminary conclusions.

I've been writing these first entries as an class assignment and while I have 26 more days in my trial of the software and plan to continue using it to analyze my usability data and report my experiences here (after I finish my other class assignments), I did have to complete and turn in the assignment I was originally writing for. This is the conclusion that I wrote for that assignment.

Several very useful points became clear as I worked with the NVivo7 trial. I began to find a number of things in my data that I did not expect to find, for instance, there are a number of behaviors evident among our users that, now that I am aware of them, will affect the way I teach library instruction. That is useful in itself but in terms of conducting qualitative research, I also see that there is a fine line that every researcher has to draw for him or herself between relevant findings and irrelevant findings. Where to draw that line would depend on the research question, the type of research design, the theoretical framework of the project and the theoretical orientation of the researcher.

My frame of mind is important to the quality of the results. It does not do much good to rush through data anlaysis. When I’m in a hurry (e.g. under a deadline to finish an analysis), I am at my least analytical and am least liable to go test an idea and am most liable to miss something in the data. Also, I tend toward linearity by nature. In this analysis of my usability data (which I started at the beginning of this semester and after all of my data collection was complete), I wanted to conduct the analysis participant by participant and then task by task rather than having to go back to participants whose transcripts I had already coded to look at them again. Both of these habits are things that I understand I will need to guard against as I continue to conduct qualitative research.

As far as the product, NVivo7 goes, it seems to me to be the most flexible and therefore useful of the QDA programs I have tested this semester. My “likes” lists outweighed my “dislikes” lists and it seems to combine most of the features that I found useful in all of the QDA programs I have tested. It seems to be intuitive enough for a novice researcher to grasp and begin using without to steep a learning curve but it also has some more advanced linking, memoing, querying, and modeling features that will satisfy a more sophisticated researcher.

The biggest drawback is the inability to code data in any format other than text (although I understand that this has been addressed in the next version). Another is that NVivo7, like most of the other QDA programs I have worked with this semester, seems oriented toward grounded theory research. I can think of two reasons why this might be so; first, because grounded theory is more systematic (at least Strauss & Corbin’s version of it) that other qualitative approaches and it is easier to develop computer programs that support this kind of thinking. But second, it may be the context of my perception that makes it seem so, given my inexperience with qualitative research the more systematic approaches like grounded theory would be easier for me to grasp and to find evidence of and uses for in the software.

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