Dig Deeper - Data Analysis
Make Sense of Textual Data
Miles, Huberman, and Saldaña's (2014) Qualitative data analysis: A methods sourcebook is an excellent resource for making sense of data generated through qualitative methods--in particular text-based data. The resource below excerpts key concepts and terms from the chapter titled "Fundamentals of Qualitative Data Analysis" to inform efforts to make sense of textual data as part of learning outcomes assessment inquiry projects.
In no way is this page intended as a substitute for the book!
"Some research methodologists believe that coding is merely technical, preparatory work for higher level thinking about the study. But we believe that coding is deep reflection about, and thus deep analysis and interpretation of, the data's meaning" (p. 72).
A suggested approach
- Read the data multiple times and take notes (in the margins, on a separate page—whatever works).
- Apply one of the first-cycle coding approaches described below (OR use a system that makes sense to you).
- Descriptive – in this is iterative, inductive, and interpretive process, the researcher assigns a word or short phrase which summarizes the basic premise or content in a passage of written text. (c.f. Annells, 2006; Denzin & Lincoln, 2005; Grandy, 2010; Hsieh & Shannon, 2005; Johnson & Christensen, 2010; Leech & Onwuegbuzie, 2007; Merriam, 1998; Schram, 2005; Strauss & Corbin, 1998; Suter, 2012; & Yilmaz, 2013).
- In Vivo – the researcher uses words or phrases drawn directly from participants’ language; note: use quotation marks to differentiate in vivo from researcher-generated codes.
- Review the results of the first-cycle open-coding (Strauss & Corbin, 1998) to identify salient themes and / or patterns; use the themes or patterns to develop axial codes (Strauss & Corbin, 1998) with which to conduct second-cycle analyses.
- Step back and consider the purpose of the inquiry. What have you learned? What surprises you?