I am still working to transcribe a mediocre to poor quality tape. I am very glad that I took notes - something I have not done much of in the past during the few in person interviews I completed - because I found myself focusing on the words of this interview as I wrote. I actually had been afraid that taking notes might diminish my focus but I think it worked in some ways to enhance it. Part of the purpose of taking notes was to help guide my follow up questions, so for that reason it was also effective. The picture above is a screenshot of the software program/language R. I have adapted R for a statistics course I am teaching this semester because it is free, it was highly recommended to me by some experienced stats instructors, it works on the main platforms, and I want to learn to use R myself. I have some experience with SAS, and find this a lot more like SAS than the widely loved program SPSS. It is pretty difficult in SPSS to have a procedure fail (unless you are one of those people who use the syntax windows) but it was, for me, uncomfortably common with SAS. R is in some ways an improvement because it will not go any further if you make a mistake (SAS seemed to be inclined to do what it could and provide error messages for the parts you messed up.)
As I see it, R is related to qualitative inquiry in (at least) two ways - one is that there is an R package for qualitative analysis. RQDA is a free add on (I installed it yesterday and am really proud of myself because it took a couple of steps to do it) developed by Wincent Ronggui Huang of Fudan University in Shanghai. Huang states that he has used this in his work to analyze print documents such as newspapers. Because RQDA is part of R, quantitative analysis of quantifiable qualitative data is very convenient. I have gotten as far as to create a practice project to see what RQDA looks like but have not yet tried it for anything. I think I will plug in my self-interview as a trial project, one of these days, when I have some free time. The other relationship (and I am more and more interested in relationships and parallels, and analogies, and the like) that I see between R and qualitative analysis is stated in the title of this post - 'precision.' It is possible to create R code in an editor window and then either paste the code into the R console (the R console is the screen I have colored pink shown in the screen shot) or use a keyboard or drop down command to execute directly from the editor. However, some document creation/word processing programs do not work well as editors. For instance, although I do know better, I wrote some code in MS Word today because I was creating a lesson plan for tomorrow's class. I pasted several lines of code and had no problem, but once I put in a line that included quotation marks (R uses quotation marks to denote string, or nominal variables), R did not recognize the fancy, symmetrical quotation marks created by Times New Roman size 12. I had to retype all of the marks to get the code to work. So, coming back around (the long way around) to where I started - whenever you, or I, translate something from one source to another, there is always the chance that the source will not recognize something or will not be able to process it (which is almost the same as not recognizing something). How much information gets lost among the many parts of data management including translation/transcription, reduction, storage, conversion, etc.? This is one of the reasons I continue to cling to my desire to transcribe my own data. If I am going to lose things, so be it (and I most definitely will) but hopefully I am more likely to recognize and even chase after the things I am most worried about losing.
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AuthorI am Sheryl L. Chatfield, Ph.D, C.T.R.S. I am a member of the faculty in the College of Public Health at Kent State University. I also Co-coordinate the Graduate Certificate in Qualitative Research and I am a member of the Design Innovation Team at Kent State. Archives
February 2024
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