Embedding NVivo in postgraduate social research training

1
Embedding NVivo in postgraduate social research
training

• Howard Davis Anne Krayer
• 6th ESRC Research Methods Festival
• 8-10 July 2014

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SXU4002 The Research Process

• Postgraduate research training module for School
  of Social Sciences
• Originally designed for ESRC-recognized MA in
  Social Research Social Policy, incremental
  changes since then
• 40 credits over 2 semesters
• Approx 15-20 students including some 3 research
• Team delivery of teaching

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Design

• Informed by the
• ESRC Research Training Guidelines 2005 -gt
• Specification for generic training
• - principles of research design and strategy
• - competence in a range of methods and tools
• - capability to manage research, including
  ethics
• - understanding alternative epistemologies
• Includes practical experience and proficiency
  in the analysis of qualitative data. (Why
  Nvivo?)

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Structure
Classroom-based Lab-based Assessment
Semester 1 Epistemology, research questions, design and strategy 1
Using quant data, sampling, questions, survey design, descriptive statistics, significance testing SPSS 2 3
Semester 2 Reflexivity, feminist research
Collecting qualitative data (observation, interviews), analysing and producing accounts, working with text and visual data NVivo 4 5
Dissertation preparation
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Integration

• A journey from
• Parallel sessions, minimal connection
  between software teaching and the research
  and design skills (bought in teaching)
• to
• More coordination of topics data collection
  data preparation category analysis coding
• writing issues presentation of data
• (in house teaching)

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NVivo software

• Many options for document preparation (plain
  text, rich text with sections, audio clips,
  pictures, pdf and audio files, RefWorks data),
• Supports inductive or deductive, in vivo or
  researcher defined, manual or automated coding
• Many retrieval options for example by node
  category, by document, text searches, matrix
• A wide range of search options including by
  attributes, folder, type of document, node
• Has dynamic links to memos, documents, and nodes
• Enables visual representations (e.g. coding
  stripes, models)
• The wide range of choices available means that
  the researcher must choose wisely amongst a set
  of tools and is by no means required to use them
  all!

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Introduction to software

• Interactive teaching style (use of videos,
  examples, discussion and hands-on experience)
• Encouraged use of help function and trying out
  different ways to do achieve a task (e.g. coding)
• Challenges
• Expectation that NVivo codes for you
• different levels of computer skills
• different levels of knowledge (and practical
  experience) of qualitative analysis approaches

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Session content (4 weeks)

• Introduction to codes and coding
• Setting up a project in NVivo 9
• Importing documents and linking to external
  sources
• Developing and working with codes in NVivo
• Assigning attributes and working with
  classification sheets
• The use of memos
• Visual presentation of data
• Queries
• Use of NVivo videos get up and running with
  NVivo, organise material into themes
  Classifying nodes

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First session

• Students substantive research interests
  interest in using qualitative methods
• PowerPoint presentation on codes and coding
  including examples from the literature
• NVivo video (getting up and running with NVivo)
• Became clear that students
• Only understand the process of
• coding in the abstract
• Started to worry about qualitative
• analysis

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Following sessions

• Use of publically available transcripts
  (Attitudes to GM crops)
• Students engaged in coding, creating
  classification sheets, setting-up queries and
  visualising the data
• Challenges
• Students did not engage with the transcripts
  sufficiently to achieve meaningful coding
• Some students struggled with the
  
  software (which
  
  button to
  press) whereas
  
  others found this easy
• Sessions are not assessed

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Linking methods teaching to NVivo

• Students need to be clear that their analysis is
  based on an appropriate methodological approach
  and a rationale for the methods of data
  collection and analysis
• Discussions in each sessions
• about how NVivo might be used with the methods
  covered in the methods teaching sessions
• of what students learned and how they might apply
  this to their own research ideas and data
• Challenges using NVivo
• Using NVivo is not an easy option and time is
• needed to read, conceptualise and analyse
  the data

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Feedback from students

• Good introduction to the software
• Can now see a link between qualitative research
  and NVivo
• NVivo is not a magic wand!
• Need to have reasons to learn how to use NVivo
• Some students were planning to use
  
  NVivo for their literature review to
• improve their skills with the
• programme and enhance
• their understanding of
• coding

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Student suggestions for improvement

• More sessions to cover a wider range of skills
• Different levels (beginner, intermediate and
  advanced)
• Introduction of specific coding approaches (e.g.
  for content analysis, grounded theory or surveys)
• Have a range of tasks students have to achieve
  and they can work through at their own speed
• Use of peer support groups

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Lessons learned

• Integration is difficult In Semester 1 the same
  teacher delivers the quantitative topics and SPSS
  which allows very good integration. Qualitative
  topics demand a wider range of experience and it
  takes longer to become familiar with discursive
  techniques.
• Pedagogic challenges linking technical knowledge
  to methods teaching and critical thinking through
  experiential learning
• Mixed-methods approach students tend to prefer
  qualitative methods as they are seen as an easier
  option
• Technical issues wide range of prior computer
  skills
• Assessment? We have resisted separate
• assessment of computer skills

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Further development

• Use the same material (e.g. a mixed-methods
  research project on attitudes to wind farms) in
  sessions on research methods as well as NVivo
  sessions to facilitate integrated learning
• Integrate some group exercises into NVivo
  teaching to facilitate peer learning
• Emphasise the importance of understanding how
  NVivo or similar programmes work to be able to
  conduct your own project but also to understand
  how others have analysed their data