Interdisciplinary and multidisciplinary working in data analysis

1. Interdisciplinary and multidisciplinary working in data analysis Nick Emmel, Kahryn Hughes, and Joanne Greenhalgh University of Leeds

2. Content • Our research and methodological approach • Complexity in interdisciplinary and multidisciplinary approaches in the research • How we do interdisciplinary and multidisciplinary research • The outputs from these research activities

4. Critical reflexivity • Two research sites: • the locality in which we do the research (a low-income urban community) • and ‘the team’ of qualitative researchers • Strategies for enhancing reflexivity: • Tape recording team meetings • demonstrating data analysis techniques to team • Writing memos to each other • Mapping thematic findings

5. Definitions of multidisciplinary and interdisciplinary working • Multidisciplinary working: working in parallel to answer a common research question but maintaining our own epistemological positions. • Inter-disciplinary working:we attempt to combine, negotiate and reconcile different epistemological positions.

7. Complexity: two types—uncertainty • Environmental uncertainty:exploratory nature of our research questions to identify and get hold of the socially excluded. • Task uncertainty: team members with different expertise “each of whom has information relevant to the solution of a particular problem but none of whom knows enough to act in isolation” (Watts, 2005:4).

8. Interdisciplinary (combined) or multidisciplinary (separate) working in the research • team meetings (combined) • iterative strategy (combined) • gathering the data (combined and separate) • reframing the data in the context of the methodologies developed (e.g., access case methodology) and producing substantive theorisations of the data (combined and separate) (current stage).

9. An example of the mapping

10. Interdisciplinary and multidisciplinary working: practice • Externalising internal strategies for making sense of data • Integration: enabling integration of different data analyses in producing explanations • Innovation: combining expertise brings the data into broader fields of visibility • Addressing complexity: these ways of working enable us to address issues of task and environmental uncertainty

11. Interdisciplinary and multidisciplinary working—some observations • Make analyses of large data-sets more manageable and creative • Bring multiple epistemologies to bear on our research questions • Address environmental and task uncertainties • Innovation is encouraged because analyses are triangulated through interdisciplinary and multidisciplinary working • Supports innovation in terms of the production of conceptual and methodological outputs that can be disseminated • While the research process is necessarily messy, it is nevertheless closely managed through these strategies of reflexivity • How the research field is constituted through multiple epistemological interests of the research team remains available for analyses throughout study • Outputs belong to each team member