1. Rachael Addicott Centre for Public Services Organisations February 2006 School of Management – Methodology and Qualitative Research Methods ANALYSING QUALITATIVE DATA

2. LECTURE OUTLINE Methods for analysing text –Words –Chunks Grounded theory Components of data analysis Qualitative analysis software –NVivo

3. INTRO TO DATA ANALYSIS Be explicit Powerful methods available Use data displays

4. METHODS FOR ANALYSING TEXT Analysing words Analysing large blocks of text

5. ANALYSING WORDS Key words in context Word counts Structural analysis & semantic networks Cognitive maps

6. ANALYSING BLOCKS OF TEXT Coding Tagging chunks of text with labels – codes – that indicate the conceptual categories the researcher wants to sort them into (Denzin and Lincoln: 804)

7. CODING Purpose of coding How do we know what to code and with what label?

8. CODING Be open to all information Do not project own feelings Do not read into the data

9. CODING Sampling Finding themes Building codebooks (eg) Marking texts Constructing models (relationships among themes) –Grounded theory

10. ROLES – role def – cm – role def – clinical – role def – man – role change – cm – role change – priorities – cm – priorities – clinical – priorities – man – ambition – cm – ambition – performance man – accountability RELATIONSHIPS – key relationships – cm – key relationships – man-clin relationships – influence – cm – influence CONTEXT – history – scale – situation – strategy CODEBOOK

11. CODEBOOK: DEFINITIONS ROLES: 1. role def – cm how the individuals fulfilling a clinical manager position define their own role 2. role def – clinical how clinicians define their role 3. role def – man how managerial representatives define their role

12. GROUNDED THEORY Inductive approach Researcher becomes more and more grounded in the data Constant comparison method Memoing!!!

13. COMPONENTS OF DATA ANALYSIS Data collection Data reduction Data display Conclusions: drawing / verifying Data collection Data reduction Data display Conclusion drawing and verification

14. TACTICS FOR GENERATING MEANING Noting patterns and themes Seeing plausibility Clustering Making metaphors Counting Making contrasts / comparisons

15. DATA QUALITY Checking for representativeness Checking for researcher effects Triangulating Testing patterns

16. FINAL ADVICE ON DATA ANALYSIS Think display (to strengthen conclusions) –Matrices –Network Be open to invention Explicit iteration Seek formalisation and distrust it Entertain mixed models Stay self-aware Share methodological learnings

17. CROSS CASE ANALYSIS Allows for some generalisability Helps to find negative cases Beware! –Maintain individuality of cases –Maintain narrative sequence

18. TO CODE OR NOT TO CODE? Don’t lose sight of the richness of the data!!! –Analyse data via code or via text unit Text (interviewees) Codes

19. INTERVIEW TRANSCRIPT - HAND CODED NOTES: Interview with Elizabeth who is an early career academic researcher at XXX University. Lynn... how did you come to be here and why are you interested in research? Elizabeth I guess you need to talk to a variety of people from a variety of backgrounds, so I will tell you. My parents are professor and associate professor of psychology, that gives you an idea that research has sort of been, and my uncle of course, was professor of mathematics, so I have grown up in this sort of very academic background, research has been just one of things that people do as long as I remember, and it was always presented as something that was interesting and exciting because you actually found out new things and people passed through our home who had discovered new things, which we talked about over the dinner tables so - This suggests that Elizabeth is sufficiently a researcher that she thinks about the method of the research she is engaging in Family background Always there Discovery Parents are clearly an important influence, since this is where she starts.

21. WHY USE SOFTWARE? Instrumental reasons –Publishing –Working with supervisor Pragmatic reasons –Manage data –Analyse data

22. WHAT CAN IT DO? Assist analysis of data  Do analysis  Build theory Importing Coding Memoing Searching Modelling

23. CHOOSING SOFTWARE What kind of computer user are you? Are you choosing for one project or for the next few years? What kind of project(s) and database(s) will you be working on? What kinds of analyses are you planning to do?

24. NVIVO Data management Coding and retrieval Theory testing Documents and nodes

25. GETTING STARTED WITH NVIVO Saving word docs as RTF files Create an NVivo project Importing documents into NVivo Creating document sets Coding Retrieving and interpreting using NVivo

26. PROBLEMS WITH NVIVO Inflexible Multiple coders Closeness to the data Rigour?

27. NVIVO PROS AND CONS Good for - Data type Interviews, focus groups, open-questions in surveys Methods Grounded theory approaches Not so good for - Data type Documentary text with many tables, graphs, pictures etc. Video-based research Methods Research on decision-making or process, where sequence is an important variable

28. FIND OUT MORE CAQDAS networking project (University of Surrey) http://caqdas.soc.surrey.ac.uk/ QSR International (free software demonstration) http://www.qsrinternational.com/