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Embedding NVivo in postgraduate social research training

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Title: 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

2
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

3
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?)

4
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
5
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)

6
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!

7
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

8
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

9
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

10
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

11
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

12
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

13
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

14
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

15
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
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