Analysing analysis

“What we do as researchers 

intervenes with the world 

and creates new possibilities 

but also evokes responsibilities” 

(Hultman & Lenz Taguchi  2010:540).

Over the past week, a sense of discomfort and a touch of anger permeated my thoughts as I reflected on recent interviews with clinician educators and students and considered ways of analysing my data. While we may take up a neutral stance in collecting data, with limited intervention as an interviewer and facilitator, the data impacts on our own being and becoming. We become immersed in the flows of relationships, in between the waves of encounters that interfere and intra-act with each other.

By recognizing affect in the data and in ourselves, our professional responsibility moves beyond contained, static and structured knowledge boundaries.  Fenwick (2014:158) suggests that it may be “more comforting to focus on human skill and [to] imagine that this can be resolved through training and discipline, rather than attempt to consider how responsibility may be distributed among the heterogenous entanglement of … material and technological assemblings”. In this sociomaterial approach “responsibility becomes reconfigured” through the “material enactments of conflict and compromise that appear in enactments of professional responsibility” (ibid). In my research, obstetric tensions are strongly related to societal inequities. There appears to be a crack or a disconnect between students who feel disheartened,  sometimes even traumatized, and others connected to educational practices who seek justification for unprofessional behaviours.

As I consider ways of analysing the growing data collected through these interviews, focus groups and drawings, the traditional method of coding to find common and recurrent themes and trends, becomes less attractive. Coding pulls together sameness through groupings and subgroupings, sometimes referred to as nodes. It offers a structured and layered genealogy that represents the information gained through the research process. If I choose a diffractive methodology that identifies differences, valuable truths can emerge that can be productive - “a different kind of knowing” (Hultman & Lenz Taguchi 2010:526).

Jackson (2013:742) argues for a posthuman ontology that engages with the entanglement of the human and non-human rather than coding that creates stabilized structures grounded on unchanging human-centred truths -- “an epistemological project flavored with humanism”. Deleuze and Guattari (cited in Hultman & Lenz Taguchi 2010:535) refer to ‘over-coded-machines” and Mazzei (2014) points out the predictability of the known in traditional coding with its consequent reductionism.

McClure (2013) draws on Deleuze’s rhizomatic thinking to note the hierarchical, arborescent nature of coding. She (2013:165) contends that coding is valuable as a “logic of representation [that] is culturally and politically significant”, however acknowledges the limitations especially associated with the dynamic relational ontology.

In terms of poststructural research, the following points suggest a need to explore alternative options for data analysis:

1. Coding happens in a safe, seemingly uncontested space as the distancing contributes to removing the researcher away from the complexity of the data, a “pull back from the data” (Mazzei 2014:743). There is distancing between the research analyst and the data.
2. The logic of representation has a contracting influence on the data. “Coding does not recognise changing speeds and intensity of relation, or multiple and mobile liaisons amongst entities” (McClure 2013:169).
3. The dynamic nature of the entanglement of data is lost in coding as it represents a fixed, limited and defined (by the researcher) reality.
4. Uncertainty is disregarded as “coding renders everything that falls within its embrace explicable” (McCLure 2013:169).
5. The act of slicing and cutting the data into groups or chunks tends to be human-centred. The interrogation of the dissected data by the researcher can lead to questions of ethical responsibility.
6. There is a sense of othering, a “colonial relation of researcher to subject” (McClure 2013:168). In addition objects tend to remain passive rather than mutually constituting meaningful data.
7. The naming of codes acts as a limiting mechanism. The dominance of language undermines the impact of affect.
8. There is privileging of a normative voice rather than a transgressive alternative according to Jackson and Mazzei (2012).

Jackson and Mazzei (2012) challenge us to think with data, to become enmeshed, immersed, and possibly unsettled as we plug in the theory with data and the data with theory. These forces and intensities attract me. I feel pulled towards postcoding in a post-qualitative framework that will allow me to ask myself, “how does the mangle move us into a different way of thinking” towards developing a socially just practice in medical education through a collaborative mutual inquiry that engages socio-material practices? (Jackson 2013:744).

Mazzei (2014), in drawing on Barad’s concept of diffraction, demonstrates the value of reading insights and meanings through each other. She recognizes that “knowing is never done in isolation but is always effected by different forces coming together” (2014:743). A diffractive, rhizomatic analysis “emphasizes difference by breaking open the data” that involves “moment[s] of plugging in, of reading-the-data-while-thinking the-theory, of entering the assemblage, of making new connectives” (ibid). There is an immersion and entanglement of ourselves and the data. There is a flow of encounters that takes place (Hultman & Lenz Taguchi 2010:537).

The image above was created with iPastels. My imported selfie image was painted over with the tools on the App on my iPad. I tried to give the impression of being caught in diffractive waves as my thoughts mingled with the audio playbacks of my recent interactions with research participants.

“[A] diffractive ‘seeing’ or ‘reading’ the data 

activates you as being part of and activated 

by the waves of relational intra-actions

between different bodies and concepts (meanings) 

in an event with the data” 

(Hultman & Lenz Taguchi 2010:537).

Fenwick, T. 2009. Rethinking professional responsibility. In Reconceptualizing professional learning: Sociomaterial knowledges, practices and responsibilities. (Eds) Fenwick & Nerland). Routledge. Abingdon.

Hultman, K. & Lenz Taguchi H. 2010. Challenging anthropocentric analysis of visual data: a relational materialist methodological approach to educational research. International Journal of Qualitative Studies in Education. 23:5:525-542.

Jackson, A. 2013. Posthumanist data analysis of mangling practices. International Journal of Qualitative Studies in Education. 26:6:741–748.

Mazzei, L. 2014. Beyond an easy sense: A diffractive analysis. Qualitative Inquiry. 20:6:742–746.

McClure, M. 2013. Classification or Wonder? Coding as an Analytic Practice in Qualitative Research. In R. Coleman & J. Ringrose (Eds). Deleuze and research methodologies. Edinburgh University Press. 164-184.