Qualitative Data Analysis and Interpretation

1. Qualitative Data Analysis and Interpretation JN602 Week 11 Veal Chapter 15, CDS Chapter 07

2. Objectives Describe the overlap between data gathering and data analysis in qualitative research Explain the difference between the analysis of data gathered by structured methods and that gathered by unstructured methods Describe the process of content analysis Describe how a computer package can assist the researcher in analysing qualitative data

3. Overlap Collection and analysis occur simultaneously Human-as-an-instrument Strength: The researcher can use the results to probe for further information and detail and Weakness: Can divert attention away from research objectives

4. Aims of qualitative analysis Understand the phenomenon Go beyond reporting � move towards INTERPRETATION Identify themes and sub-themes

5. Data storage and confidentiality Because qualitative data may include personal opinions and details: Security of data storage is important Ideally, pseudonyms/codes should be used even with stored data/transcripts etc. Efforts should be made to protect confidentiality/ anonymity of informants when reporting results

6. Structured methods Use pre-planned questions from structured interview or focus group Identify common responses within each question May still have some variety that will need content analysis (unstructured method)

7. Quantifying methods Informal methods: identify repetitive or patterned behaviour Frequencies Content analysis: converting text to numerical variables. Use coding units - words, themes, items, time Repertory grid: mental maps

8. Example - Frequencies

9. Content analysis The process of identifying, coding and categorising the primary patterns in the data Constant comparative analysis reads raw data and identifies an important point Continues reading and identifies another point Compares to first point and so on

10. Content analysis process (1) Prepare and organise raw data Source code all raw data Copy raw data Store originals of raw data in safe place Read Theme coding system Compare first theme with second theme and so on

11. Content analysis process (2) Data index and classification (coding frame) Transfer indicated passages to a file Open coding Axial coding Rules for inclusion Selective coding Mapping Write report

12. Preparation stages Prepare and organise raw data � transcribe information and audio material Source code all raw data � identify where the information was originally obtained Example - IA3b4: Interview, with Administrator 3, in the second interview, from page 4 of the transcript Copy raw textual data - tends to get marked and destroyed Store originals of raw data in safe place � filing cabinet, locker � secure location required Read through notes � first take, to get overall picture of what you have seen.

13. Reading + Emergent themes Reading The key activity in qualitative data analysis is reading and re-reading the material Reading begins with initial research questions/models etc. in mind � but evolves Emergent themes Ideas/concepts which emerge are referred to as �emergent themes� For one scenario, see: Fig. 15.2 � Initial outline conceptual framework Fig. 15.3 � Annotated interview transcripts Fig. 15.4 � Further developed conceptual framework

14. Outline/Initial/Simple conceptual framework

15. Interview transcript extract � annotated � Fig. 15.3 (p. 296)

16. Partially developed conceptual framework � Fig. 15.4

17. Mechanics Annotate transcripts with �themes� � as in Fig. 15.3 Need to leave wide margins or use �columns� Colour coding may be helpful Word-processor may be used to: Add comments/block text in colour, underline or bold Search for words/phrases Code and cross-reference using indexing Numbering paragraphs may be useful for cataloguing Eg. Career attitude-strategic - Mark: p. 2, para. 3; p. 7, para 4; Jennie: p. 7, para. 1

18. 6 � 9 Open coding First pass through data Study field notes. Locate themes, assign initial codes or labels (step 6) Themes comes from initial question, literature, or from the data. Similar to a filing system Aim is to reduce data to manageable categories

19. Axial coding Second pass through data. Focus on initial coded themes. Determine consequences, conditions, interactions, processes�. Seek to identify causal patterns in the data

20. Six Ways to Discover Patterns Frequencies Magnitudes Structures Processes Causes Consequences

21. Rules for inclusion Properties or characteristics of passages in the data that identify it as relevant to that category i.e. What is included, what is excluded: May occur at open or axial coding stage

22. Selective coding Third/last pass through data. Involves scanning data and previous codes. Look for evidence to support themes developed E.g. text samples Identify major themes of research, and contrast between themes. Can involve collapsing themes together (e.g. is there a need for separate categories of seating)

23. Unstructured procedure Convert field notes into written record (reference field notes) Code data to allow storage and retrieval Write summaries at various stages Use summaries to construct generalisations to confront existing theories or construct new theories

24. Mind mapping �Mind maps were developed in the late 60s by Tony Buzan as a way of helping students make notes that used only key words and images. They are much quicker to make, and because of their visual quality much easier to remember and review. The non-linear nature of mind maps makes it easy to link and cross-reference different elements of the map.� www.peterussell.com

25. Example of mind maps Lecture: http://www.jcu.edu.au/studying/services/studyskills/mindmap/samplelecture.html Website: http://www.peterussell.com/MindMaps/mindmap.php About Mindmapping: Tony Buzan http://www.youtube.com/watch?v=MlabrWv25qQ

26. How to mind map (Russell, 1997) Use just key words, or wherever possible images. Start from the center of the page and work out. Make the center a clear and strong visual image that depicts the general theme of the map. Create sub-centers for sub-themes. Put key words on lines. This reinforces structure of notes. Print rather than write in script. It makes them more readable and memorable. Lower case is more visually distinctive (and better remembered) than upper case. Use color to depict themes, associations and to make things stand out. Anything that stands out on the page will stand out in your mind. Think three-dimensionally. Use arrows, icons or other visual aids to show links between different elements. Don't get stuck in one area. If you dry up in one area go to another branch. Put ideas down as they occur, wherever they fit. Don't judge or hold back. Break boundaries. If you run out of space, don't start a new sheet; paste more paper onto the map. (Break the 8x11 mentality.) Be creative. Creativity aids memory. Get involved. Have fun.

27. Displaying qualitative data Often qualitative data can be best represented through visual methods Matrices: e.g. events flow matrix, effects matrix Charts and graphs Mapping: generate conceptual frameworks from themes

28. Effects matrix

29. �Crosstabulation� of qualitative data � Fig. 15.5

30. Mapping example � CDS Fig.7.8

31. Using a computer package Can only assist human judgement E.g. Nvivo, NUD*IST

32. The qualitative analysis process Overlap between gathering and analysis Manifest vs latent content Decisions are yours Gathering data, analysing data and writing report are not mutually exclusive Need to recognise and account for the role of the researcher in the analysis