I have been writing about, so thinking about, the role of qualitative data analysis instruction in the overall process of learning how to do qualitative inquiry.
During the past year or so, I have shared with a few people my increasing interest in teaching with an analysis-first or analysis-nearly-first approach. This mostly came about after I read Qualitative research analyzing life by Saldaña and Omasta. As a result, I have had questions from colleagues because I have moved away from an interview-first approach. "You need to know how to get data," people have said, "before you can start on analysis." I've given this some thought and wondered, in comparison: Is it necessary to know how to build a bicycle, in order to ride a bicycle?
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The idea of creating/refining algorithms to perform some version of qualitative coding is not new or novel, although the practice does not seem yet to be widespread enough for people to consider it routine. I suspect this is due in part to the fact that many people who do research have been trained to be project managers, rather than technicians, and many technicians focus on methods for their own sake, so are not in the habit of thinking through research questions and designs to the extent needed to publish or fund a project. And, as an aside, I like to think of myself as a little more of a generalist or maybe "working supervisor" is more apt. I also think methods training is more important in a graduate degree than subject matter training. I suspect many of my colleagues differ in their views. But, to return to my subject matter of interest for this post - I want to begin by acknowledging that many qualitative researchers have described, in writing or in conversation, the reasons why a program cannot code or analyze data as well as a human. A recent experience with (human) coding of a biggish data set reminded me of several of these.
I have been struggling for a few days to copy some text information from a moderately large Excel spreadsheet - it has 1200 + rows and the columns go from A to BO - using Word's mail merge function.
I first discovered the joy of mail merge when I was creating vignettes from an Excel data frame I made using R. I used R to randomly order category levels and printed the options to Excel. Then I added some stem wording in Word, and was able to end up with what looked like a paragraph of text but was really a fairly complex multiple regression equation with a lot of categorical predictor variables. Once I started to do mixed methods secondary analysis with certain datasets from the Centers for Disease Control and Prevention, I began copying and pasting narrative data from Excel into Word to improve my ability to read and apply qualitative coding processes. Then one day - and I do not know why this idea suddenly came into my mind - I realized that I could also use mail merge to print the information from the cells I wanted, into a series of Word documents. I did this over the summer in bundles of 100 with no real problems. I was working in 100s because I wanted to give data to my student research assistant incrementally. However, earlier this week when I tried to print the content from just 3 columns of all 1200 + rows, I immediately got error messages, and only a portion of the cases actually printed (not in order, unfortunately). I also got a lot of this: One time, many years ago, I prepared some pasta as part of a meal for some friends. This group included a much older man who moved with his family from Italy to the US when he was a child in the early part of the 20th century. He was also an excellent cook, and very passionate and particular about the quality of the food he ate. He took one bite of the pasta - which was probably linguini or fettucini - and said to me "You added more water to the pan after the pasta started to cook, didn't you?" The answer to that question was "yes," and this happened because I had underestimated the amount of water needed and what was in the pan was not ample to cover the pasta. But I was, still am, very impressed that he could tell that from a single bite. I think my ability to figure out whether pasta is appropriately cooked has improved (although I have known a lot of people who prefer noodles mushier than my standard preparation) but I'm not certain if I have fine tuned my tasting ability enough to recognized if the cooking process was interrupted or restarted. I thought of this incident today as I considered a few research papers I have reviewed in recent months - where I think the research process was interrupted or restarted. (Right now, by the way, based on requests I see and the progress of my own works, it seems like everyone has time to write or revise but no one has time to review!) There is something very compelling about reading a work that has clear aims and appears to be "coherent" (this is a word the TQR editors use to describe cohesion or consistency among all of the aspects of research) from the purpose through to the end. As an aside, my recent efforts with secondary analysis have shown that full coherence is difficult to achieve, so you do what you can and honestly offer up the limitations, which should be weighed against the efficiencies inherent in secondary analysis. But for a planned, purposive research study, coherence seems like it should be a given.
I have worked with some larger than usual (for qualitative inquiry) datasets through the course of a few projects that used CDC National Violent Death Reporting System (NVDRS) data. Sample sizes have been in the hundreds but case details are typically brief. The data also generally focus on a single (violent) incident although fortunately for qualitative researchers, there is often some contextual detail - although the amount varies from one case to the next. This summer I was provided with multiple opportunities to work with even larger samples - including some that include more than 1,000 cases. Like NVDRS data, case lengths vary and tend to be short. There are also clear categorical patterns that will allow for some quicker analyses - although how many things fit depends on how broadly you define a given pattern. Basically, I am trying to balance use of some automation and programming with hands on engagement with the data. I expect to have a few, maybe several more comments as these projects continue, but the point of this post is to describe my experience using the Microsoft Excel fill handles to copy data. I just re-read Longitudinal Qualitative Research Analyzing Change through Time by Johnny Saldaña (Altamira, 2003). I have had this book for a couple of years but am just now considering details of a longitudinal qualitative project, so I read it seriously and have just begun another work I will probably post about in the near future. One of the recommendations Saldaña made for researcher trying to understand change over time was to ask people directly how they have changed. This is one of those amazingly common sense things - that I have heard in other forms through the years (e.g., if you want to know how someone has been successful at something, ask them how they have been successful at that thing) although I realized that I probably do not do this nearly enough. How many interview guides steer all around the issue without coming right out and asking about it? How many times is the research question one of the interview questions?
I have finally updated the 'research and presentations' page. I uploaded all of my prior TQR presentation slides, plus the abstract for a panel presentation accepted for 2021.
I listed papers under review, and one recent publication, plus one "in progress." I added some comments under each - to provide a little info about the story behind each work. This is something you never see on a CV, although I suspect every paper has some type of story..... I have been a member of the Mixed Methods International Research Association (MMIRA) for a few years now. It is a truly international interest group with chapters representing a variety of countries or regions, and a governing board of international scholars. One of the best benefits is the mixed methods MOOC. One reason I am a fan is that I have just completed a module (on mixed methods secondary analysis), and another is that I have not had access to many academic courses on mixed methods. I have completed a couple of the modules in the first two seasons, and I was able to virtually, asynchronously attend courses taught by known scholars including Elizabeth Creamer, Joseph Maxwell, and Jennifer Greene. Membership is required for access to the MOOC, but membership costs, especially student ($25) are pretty reasonable. When you consider the cost of some single session online workshops, having access to the entire expanding MMIRA MOOC for the basic fee of $65 is a pretty good deal.
I came upon this sentence today, while reading "Decolonizing methodologies - research and indigenous peoples" by Linda Tuhiwai Smith:
"the belief in the ideal that benefitting mankind is indeed a primary outcome of scientific research is as much a reflection of ideology as it is of academic training" (p. 14) (emphasis added). Unfortunately a lot of research is built around funding opportunities, personal, sometimes selfish interest, and research fads rather than needs. And a lot of research does not, or does not directly benefit people who have limited opportunities, or poor health, or face other profound challenges that prevent them from living comfortably and safely. I happened upon an online article the other day about the "Karen" meme. (As an infrequent-to-none user of social media, clearly I do not pick up on trends right away!) I am not a "Karen" - gen X female with blond asymmetrical bob haircut but I still was bothered by this, as well as the earlier "Shut up, Boomer" trend. Back in my childhood people said "Never trust anyone over 30." Since I was about 5 or 6 at the time, and 30 seemed absolutely ancient, I did not pay any particular attention to this.
When stereotypes are mean-spirited, applied without consideration of individual differences, and are used to dismiss or undermine anything a person has to offer, I think any potential humor is diminished while intentional or unintended negative consequences are enhanced. |
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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