Comparing analysis frames for visual data sets: using Pupil Views Templates to explore perspectives of learning

Kate Wall, Steve Higgins, Richard Remedios, Victoria Rafferty, Lucy Tiplady

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


A key challenge of visual methodology is how to combine large-scale qualitative data sets with epistemologically acceptable and rigorous analysis techniques. The authors argue that a pragmatic approach drawing on ideas from mixed methods is helpful to open up the full potential of visual data. However, before one starts to "mix" the stages of analysis one needs to be aware of the strengths and weaknesses provided by the various qualitative and quantitative perspectives. This article therefore provides a methodological discussion based on empirical research experiences with one visual data set: Pupil Views Templates (Wall and Higgins). The authors investigate two different approaches to the analysis of these data: inductive and deductive processes. The two approaches are applied separately to the same data set and observations are made regarding the affordances and constraints of each process, and the findings and implications for developing visual analysis in this area are presented. The authors show how both processes provide useful insight, but without clear strategy as to how they can be combined to achieve the intent of the research, the true potential of visual data will remain unlocked.
Original languageEnglish
Pages (from-to)22-42
Number of pages21
JournalJournal of Mixed Methods Research
Issue number1
Early online date19 Jul 2012
Publication statusPublished - 1 Jan 2013


  • mixed methods analysis
  • visual methods
  • Pupil Views Templates
  • learning to learn


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