Research is a juggling act. I think to be a successful researcher you must juggle at any given time at least four of the following: data collection, reading, writing, analysis, reflexivity -- always reflexivity and reflection. Am I leaving anything out? Probably.
This whole semester I thought I was doing well, when, in fact, at most I was tossing around maybe two things at a time. Big deal.
After reading the coding and analysis literature this week, I realize I have dropped a major ball, the analysis one.
I also realize I have some new questions. (See end of post for questions).
Coding, analysis, and interpretation
Coding is a commonly accepted approach to analysis, and, as such, it has its share of critics, who claim it is an over-glorified form of reductionist frequency counting.
In the opening chapters of the new edition of his coding manual, Saldaña (2013) addresses the critics. He says coding is not the end-all, be-all of analysis. Nor is it a discrete stage of analysis done in isolation from other aspects of research.
Saldaña argues that coding does not distance the researcher from his or her data:
If you are doing your job right as a qualitative researcher, nothing could be further from the truth. Coding well requires that you reflect deeply on the meanings of each and every datum. Coding well requires that you read and reread and reread yet again as you code, recode, and recode yet again. Coding well leads to total immersion In your data corpus with the outcome being exponential and intimate familiarity with its detail, subtleties, and nuances. (p. 39)I so need to do this. I haven't been handling the data well at all. I have been amassing it, stockpiling it, peering at it occasionally with heavy heart as I do with hampers of dirty laundry. In fact, I now have piles of neatly folded, laundered clothes, and no grasp of my study. I will choose laundry any day over the challenge of data analysis.
The readings on coding and analysis cause me to feel a pit in my stomach as I realize how far removed I have become from the actual texts of my study. I haven't transcribed in weeks. I haven't touched, much less reflected on, field notes, emails, and other artifacts from last semester. I acquired a thick folder of documentation from one of the key informants for my study, and the documents still sit where I left them two weeks ago.
Worse, I have been continually conducting interviews with participants for the last three months, without the benefit of immersion in the data to guide or shape my interactions with those participants.
In their discussion on the process of line-by-line microanalysis, Strauss and Corbin (1998) write, "We are moved through microanalysis by asking questions, lots of them, some general but others more specific. Some of these questions may be descriptive, helping us ask better interview questions during the subsequent interviews" (p. 66).
I should be forming theoretical questions that probe relationships between concepts and then asking these question during follow-up interviews.
I just concluded the first round of interviews. Time is running out for follow-ups, as my participants are classroom teachers who will not relish being interviewed over summer vacation. This is a potential problem.
Analytic memos
And because I have not been immersed in the data, I also have not generated a single analytic memo about the data, not since last semester. This is another problem.
Analytic memos are the raw materials for what will ultimately become the “theoretic text,” in which the researcher “finally sees the theoretic interpretation – core thesis – he or she wants to put forward” (Piantanida & Garman, 2009, Ch. 13, para 1). They are initiated by “aha moments” and “conceptual leaps” that put “the myriad individual, idiosyncratic, and situational details into a meaningful, coherent, theoretic perspective” (para 2).
According to Saldaña, "Virtually every qualitative research methodologist agrees: whenever anything related to and significant about the coding or analysis of the data comes to mind, stop whatever you are doing and write a memo about it immediately" (p. 42).
Richardson (1994) refers to analytic memos as "theoretical notes." These are the researcher's hunches, hypotheses, connections, and/or critiques about what is being seen and heard in the field. The researcher opens up his or her texts to interpretation and a "critical epistemological stance" (p. 526).
Memo writing goes hand-in-hand with analysis. All sorts of memos may be generated during research, but the analytic ones mark the researcher's first attempts at creating findings.
In writing workshop, instructors guide their students: "Don't get it right, get it writ." This advice applies to the research memo as well. Saldaña says just write the memo; worry about the title and category later. He explains,
I simply write what is going through my mind, then determine what type of memo I have written to title it and thus later determine its place in the data corpus. Yes, memos are data; and as such they, too, can be coded, categorized, and searched with CAQDAS programs. Dating each memo helps keep track of the evolution of your study. Giving each memo a descriptive title and evocative subtitle enables you to classify it and later retrieve it through a CAQDAS search. (p. 42)Grounded Theory and ATLAS.ti
One thing that has become more transparent to me in the coding and analysis readings is the connection between grounded theory and ATLAS.ti.
Some researchers distrust CAQDAS tools because “enduring foundationalist epistemologies are clearly being drawn on in their design and programming” (Brown, 2002). After reading portions of Strauss and Corbin’s (1998) handbook on grounded theory, I finally see what the critics are talking about. Key features of ATLAS seem to be borrowed directly from the grounded theory genre: open codes, in vivo codes, network views (Strauss and Corbin call them “diagrams”), and the integration of memos.
Strauss and Corbin did not claim to know much about computers in their 1998 volume, but they specifically mention ATLAS.ti as “more geared toward theory building” (p. 276) and reproduce a memo from ATLAS developer Heiner Legeiwe that says as much.
Questions:
- Both the grounded theory guidelines as well as Saldaña’s book refer to the use of categories. How does one denote categories in ATLAS.ti? I have been using prefixes to organize codes upfront, so my prefixes are definitely not categorical. They are simply organizational/topical in nature. Is this what code families are for? Does it matter?
- Saldaña refers to “subcodes” and “subcategories.” How can these be represented in ATLAS.ti?
Brown, D. (2002). Going digital and staying qualitative: Some alternative strategies for digitizing the qualitative research process. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 3(2). Retrieved from http://www.qualitative-research.net/index.php/fqs/article/viewArticle/851
Piantanida, M., & Garman, N. B. (2009). The qualitative dissertation: A guide for students and faculty (2nd ed., Kindle version.). Thousand Oaks, CA: Corwin.
Richardson, L. (1994). Writing: A method of inquiry. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 516–529). Thousand Oaks, CA: SAGE Publications, Inc.
Strauss, A., & Corbin, J. M. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory. Thousand Oaks, CA: SAGE Publications.
Saldaña, J. (2013). The coding manual for qualitative researchers (2nd ed.). Los Angeles: SAGE Publications Ltd.
