Abstractions and theorizing

Grbich’s (2007) section on writing up data seemed at once both simplistic and helpful. What does that say about me that I reacted to the functional nature of Part Four? It’s probably related to the pragmatic research posture I am exploring…

Theorizing from Data (chapter 14) was the most helpful chapter, although certainly dealing with the complexity of theorizing from an elementary level. I have not viewed theory from this micro, middle, and grand level in the way that Grbich presents it. She speaks of these choices in the writing up of our research with terms like “theory directed” and “light theoretical interpretations” (p. 185) which makes me feel as if I am deciding to order an entrée or select my meal in an ala carte fashion! I’ll take the lightly theorized phenomenology with a side of heavily postpositivist multiple regression, please.

I do appreciate Grbich’s general style of presenting examples to highlight each of her categories in this and other parts of her text. I suppose she had to include examples from a variety of disciplines (business, management, sociology) in order to appeal to a larger qualitative research community. The lessons learned from this chapter came mostly in the form of illumination of the underlying assumptions of each level of theorizing and abstracting. My claims in this arena must be logically connected to the methodology my studies follow.

I took many cautions to heart from the multiple methods chapter. (When you have a hammer…) In particular, my own research design for my dissertation study will be improved as a result of the “Advantages of combining quantitative and qualitative results” section (p. 197). I am particularly attracted to the idea of using quantitative data to examine differences between groups (variability and covariability) while tracking changes over time (a yearlong teacher internship process) and qualitative data (interviews and focus groups) to approach an understanding of the experience for the participants. Grbich has helped me to conceptualize the frame for this data collection more clearly and to hopefully avoid some of the pitfalls of poorly designed mixed methods research.