Chapter 6
Coding Strategies
Coding is analysis. To review a set of field notes, transcribed or synthesized, and to dissect them meaningfully, while keeping the relations between the parts intact, is the stuff of analysis.
Miles and Huberman, An Expanded Sourcebook: Qualitative Data Analysis, p. 56.
Defining coding as a process, doesn't begin to describe the cognitive processes we engage in when we code data. The coding strategies described below provide only a broad and general approach to analyzing your data. And some strategies will be more or less appropriate to your particular analysis than others. The choice of emphasis and interpretation is yours! At the bottom of this page you'll find some useful book titles and web-based papers that discuss other strategies for coding your data.
Your Research Questions
Before you begin any coding dig out your research questions and spend some time thinking about them. Most, but not necessarily all of your coding efforts should emanate from your research questions. Sometimes the most exciting part of your analysis emerges as the result of unforeseen and unanticipated discoveries you encounter in your data.
As we discussed in Chapter 2, Working with Nodes, node addresses, names (aka CODES) and Definitions are all Node Properties. Defining a Node or a Code helps you refine your thinking about your codes as you progress through your analysis. Discipline yourself to define each of your codes using the Node Properties button on the Node Explorer and you'll be surprised how your definitions can change over time as you become more deeply engaged with your data.
Coding WHAT vs. Coding HOW
Sometimes your coding strategy will focus on the WHAT -the nature of the content you are coding- and sometimes your strategy will focus on the HOW -the way your approach your coding. It is useful to think about the what and the how as think about how the strategies described below might be useful (or not) to your study.
Automate Your Coding
You can automate some of your coding by using string and pattern searches (Chapter 7) to locate keywords in your documents. The caveat, of course, is that often the more dynamic and abstract concepts cannot be characterized by a single word or phrase and thus you shouldn't rely on keyword searches to provide the foundation for your analysis. That said, it's an effective strategy to begin honing in on some of the broad concepts in your analysis.
Generative / Conceptual / Thematic Coding / Open Coding
Generative or OPEN coding as the term is used in Grounded Theory methodology, is the process of developing categories of concepts, and themes emerging from your data. It is an 'open' process in that you engage in exploration of your data without making any prior assumptions about what you might discover.
Axial coding, a term generally used in the context of Grounded Theory, facilitates building connections within categories - that is, between categories and sub-categories, and thus serves to deepen the theoretical framework underpinning your analysis.
Selective coding, also a term generally used in the context of Grounded Theory, is reflected in the structural relationship between your categories - the relationship between a core category and related categories - which are integrated to form the theoretical structure of your analysis.
Factual coding represents ideas that lean more toward the concrete; - such as Actions, Definitions, Events, Properties, Settings, Conditions, Processes, etc. Some might call this Descriptive coding.
Interpretive coding focuses on more abstract issues and concerns such as Causal Conditions, Perspectives, etc.
Looking for patterns in your data can be understood in many different ways
and different researchers offer a variety of interpretations of pattern analysis.
In her paper, Integrating
Data: Can Qualitative Software Do It? (pdf) Lyn Richards, offers
one perspective. Pattern analysis can focus on conceptual relationships, chronologies
and taxonomies, language analysis, or repetitions. It typically does not involve
counting instances of a phenomenon. This would be more characteristic of Content
Analysis. You may still find some "old school" researchers who
believe that Content Analysis lies at the heart of of qualitative research
however, its roots are found within the quantitative paradigm.
Tip:
A great online source for new, used
and out of print books is AddALL,
which searches several online book stores at once. Be careful!! Don't get
hooked!!
Books on Data Analysis and Coding
Fielding, N. G. and Lee, R. M. (1991). Computer Analysis and Qualitative Research. Sage.
Kelle, U. (Ed). (1995). Computer-Aided Qualitative Data Analysis Theory, Methods and Practice. Sage.
Miles, Matthew B. and Huberman, A. Michael. Qualitative Data Analysis An Expanded Sourcebook. Second Edition, Sage.
Patton, M. Q. (2001). Qualitative evaluation and research methods, Third Edition. Sage.
Tesch, R. (1990). Qualitative Research: Analysis Types and Software Tools. Falmer. (Out of print).
Web Resources on Data Analysis and Coding
- Analytic Induction as a Qualitative Research Method of Analysis
- Donald Ratcliff, Biola University, CA
Fifteen Methods of Data Analysis- Donald Ratcliff briefly discusses 15 methods of data analysis including: typology, taxonomy, constant comparison/Grounded Theory, analytic induction, logical analysis/matrix analysis, quasi-statistics, event analysis/microanalysis, metaphorical analysis, domain analysis, hermeneutical analysis, discourse analysis, semiotics, content analysis, phenomenology/heuristic analysis, and narrative analysis.
Techniques to Identify Themes in Qualitative Data- By Gery W. Ryan and H. Russell Bernard
Theory Building in Qualitative Research and Computer Programs for the Management of Textual Data- Kelle, U. (1997). Sociological Research Online, Vol.
2, No. 2.
See Section 2: What are the basic Functions of 'Computer Programs for Qualitative Analysis'?
Qualitative Data, Quantitative Analysis- By H. Russell Bernard, University of Florida
Varieties of Qualitative Research Analysis- Four approaches to analysis adapted from Tesch by Donald
Ratcliff, Biola University, CA.
Copyright
Bobbi A. Kerlin, Ph.D.
http://kerlins.net/bobbi/research/nudist/
Last updated
February 11, 2002