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 • Approx15-20 students including some +3 research • Team delivery of teaching

3. Design • Informed by the ESRC Research Training Guidelines 2005 -> • 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

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