Qualitative Data Analysis

1. Qualitative Data Analysis With QSR NVivo Graham R Gibbs and Kathryn Sharratt

2. QSR NVivo • Developed by Lyn and Tom Richards in Australia. • Started as NUD.IST in 1980s. Now NVivo v. 10.

3. NVivo at Huddersfield • The University now has a site licence for NVivo. • NVivo now on all HHS PC lab computers, classroom computers and staff office computers. • NVivo available for staff to install on their own computer at home. Go to the IT help desk in the Library, you will be able to borrow the install disk. • on the University UniDesktop. NVivo generally works well but video playback is far too slow to be useable. Other media, such as audio, pdf and Word docs are OK

5. NVivo video • http://youtu.be/oelXFnJ-7Ms

6. Getting help • QSR website • Tutorials (also on YouTube) • Help system (also from the program) • Discussion lists (answered by QSR staff) • CAQDAS Networking project, U. Surrey • For advanced uses • Online QDA • For info on basic qualitative data analysis

7. Types of Qualitative analysis • Ethnography • Analytic Induction • Content analysis. • Thematic analysis • Grounded Theory • Phenomenology • Narrative and biography • Conversation analysis • Discourse analysis

8. Induction vs. Deduction • Induction - theories and explanations derived from the data. Data led • Deduction - theories and explanations derived from theories and then tested against the data. Theory led. • Most qualitative analysis approaches are inductive (e.g. Grounded Theory, Analytic induction). • But we can also test theories against our data.

9. Preparation

10. Transcription • Kvale warns us to “beware of transcripts”. • Dangers = • superficial coding • decontextualization • missing what came before and after the respondent’s account • missing what the larger conversation was about • Transcription is a change of medium

11. Format of transcript • Names. Use capitals for speakers • MARY C • MARY • I: • or “IV:” • or “INT • In NVivo, keep name of speaker in separate paragraph.

12. Anonymisation • Names and contextual names (places etc) • Keep original with real names, but keep secure. • Publish only anonymised versions

13. Prepare text • Check for accuracy. • Use […] for missing text • Use [bribery?] for words you are not sure about. • Print with wide margins (for next stage, coding)

14. Levels of transcription • People don’t speak in sentences • Repeat themselves • Hesitate, stutter • Use contractions (don’t, coz, etc) • Use filler words (like, y’know, er, I mean) • Options • Just the gist • Verbatim • Verbatim with dialect • Discourse level.

15. Just the gist “90% of my communication is with … the Sales Director. 1% of his communication is with me. I try to be one step ahead, I get things ready, … because he jumps from one … project to another. …This morning we did Essex, this afternoon we did BT, and we haven't even finished Essex yet.”(… indicates omitted speech)

16. Verbatim “I don’t really know. I’ve a feeling that they’re allowed to let their emotions show better. I think bereavement is part of their religion and culture. They tend to be more religious anyway. I’m not from a religious family, so I don’t know that side of it.”

17. Verbatim with dialect “‘s just that – one o’ staff – they wind everybody up, I mean, – cos I asked for some money – out o’ the safe, cos they only keep money in the safe – ’s our money – so I asked for some money and they wouldn’t give it me – an’ I snatched this tenner what was mine.”

18. Conversation analysis Bashir: Did you ever (.) personally assist him with the writing of his book. (0.8) Princess: A lot of people.hhh ((clears throat)) saw the distress that my life was in. (.) And they felt it was a supportive thing to help (0.2) in the way that they did.

19. Sources in NVivo Can add: • Word documents (doc, docx) and editable • RTF files (.rtf) and editable • PDF files (.pdf) • Audio files (.mp3, .wav) • Movie files (.wmv, .mp4) • Web pages (as pdf via NCapture in IE or Chrome) • Survey data (spreadsheet format)

20. Variable data • Called attributes in NVivo • Attached to cases (normally = people) • E.g. occupation, gender, age, birth town • i.e. categorical data or measurements • Sort out cases • Put data into a spreadsheet (first column = case names, first row = attribute names, cells =values) • Import as a Classification Sheet.

21. Exercise 1 • Prepare an interview. Use BarryT.doc • Fix speaker names, new lines, capitals, spelling. • Save • Start NVivo. • Create new project, and import BarryT.doc and two pdfs • Save your project

22. Analysis

23. Thematic Coding • Grounded Theory (Glaser and Strauss + Corbin + Charmaz) • Interpretative Phenomenological Analysis (Jonathon Smith) • Template analysis (Nigel King) • Framework analysis (Ritchie and Lewis) • All are types of thematic analysis.

24. Bryman suggests these stages Stage 1 • Read the text as a whole, Make notes at the end • Look for what it is about • Major themes • Unusual issues, events etc • Group cases into types or categories (may reflect research question – e.g. male and female)

25. Stage 2. Read again • Mark the text (underline, circle, highlight) • Marginal notes/ annotations • Labels for codes • Highlight Key words • Note Analytic ideas suggested.

26. Stage 3. Code the text • Systematically mark the text • Indicate what chunks of text are about – themes – Index them. • Review the codes. • Eliminate repetition and similar codes (combine) • Think of groupings • May have lots of different codes (Don’t worry at early stage – can be reduced later)

27. Stage 4. Relate general theoretical ideas to the text. • Coding is only part of analysis • You must add your interpretation. • Identify significance for respondents • Interconnections between codes • Relation of codes to research question and research literature.

28. Coding in NVivo • Codes are known as Nodes • Coding to nodes by: • Select text, then • Drag and drop • Fast coding bar (with menu of nodes) • Menu and dialog box (can code at multiple nodes)

29. How is coding done? Age contrast Residence focus Young find work easily Word of mouth Contrast situation Constrained

30. Applying the codes to the data • Need to take code and its definition and apply in standard way to the text. • Identify chunks of text to which code applies • Can be phrases, sentences, several sentences or even paragraphs • Coded passages may overlap

31. Questions to ask • "What is going on? • What are people doing? • What is the person saying? • What do these actions and statements take for granted? • How do structure and context serve to support, maintain, impede or change these actions and statements?" (Charmaz 2003: 94-95)

32. Exercise 2 • Do some coding • Create a new nodes “Family”“Self care”“Care service” (Node in Create ribbon) • Use drag and drop to code some text to these nodes (look at first three pages) • Read some text and create new node from selected text using quick coding bar. • Code several paragraphs to this node and then uncode some irrelevant segments.

33. Coding supports 2 forms of analysis • Retrieval • Using the coding frame

34. 1. Retrieval • Retrieve all the text coded with the same label = all passages about the same phenomenon, idea, explanation or activity - Literally cut and paste • Used envelopes/files - Now done using software – retrieval very fast. • Enables cross case comparison on same theme.

35. 2. Using the coding frame • Use the list of codes to examine further kinds of analytic questions, e.g. • relationships between the codes (and the text they code) • grouping cases

36. Exercise 3 • Examine text that has been coded • Show nodes pane • Double click on a node to retrieve text coded to that node. • Open BarryT document, and show coding stripes. • Right click on stripe to retrieve text (Open Node)

37. Data driven or concept driven? • Inductive or deductive • Most qualitative analysis does both • i.e. start with some theoretical ideas • these derived from literature, research brief/questions, interview schedule • and • discover new ideas, theories, explanations in the data.

38. Code list, scheme, frame, template • List of codes with definitions • Separate from the documents • May be hierarchical Used: • To apply the code in a consistent way. • To share codes with others, especially in a team

39. Code Definitions Typically records: • The label or name of the code. • The name of the researcher. (Not needed if you are working alone.) • Date when coding was done or changed. • Definition of the code. Analytic idea it refers to. • Other notes about the code, e.g. • ideas about how it relates to other codes • a hunch that the text could be split between two different codes.

40. Exercise 4 • Inspect node properties (Right click on node, or select node and Properties from Home ribbon) • Add a description and change its colour

41. Coding hierarchy • Codes can be arranged in a hierarchy e.g. with these codes from a study of friendship • Close, generalised friendships • Sporting friendships • Sports club members • Work friends • Making new friends - same sex • Making new friends - different sex • Losing touch with friends • Becoming sexual relationships

42. Example code hierarchy • Friendship types • Close, generalized • Sporting • Club • Non-club • Work • Changes in Friendship • Making new friends • New same sex friends • New different sex friends • Losing touch • Becoming sexual relationships

43. Exercise 5 • Create a code hierarchy • Open Nodes pane. • Use drag and drop, copy and paste and new node to create a hierarchy. • Open and close hierarchy by clicking on + or – sign. • Use cut and paste to merge the coding at two nodes.

44. Memos • Theorizing and commenting about codes as you go along • Notes to yourself “… the theorizing write-up of ideas about codes and their relationships as they strike the analyst while coding… it can be a sentence, a paragraph or a few pages… it exhausts the analyst’s momentary ideation based on data with perhaps a little conceptual elaboration.” • Glaser, B.G. (1978) Theoretical Sensitivity: Advances in the methodology of grounded theory. Mill Valley CA: Sociology Press.

45. An Example Memo Word of mouth was mentioned by Harry as important for him in searching for work. Several other respondents talked about this as a method they have used. Two thoughts occur to me. • To what extent is this a separate method of looking for work, tapping into a network outside the formal one of job centres, agencies etc. or does it overlap? E.g. is some of the word of mouth information about the formal job finding agencies? • Does it refer to a specific kind of network - mates and relatives finding work for those looking for it, or is it simply a passing on of information that could have been found by those looking in newspapers ads etc? Above all it raises issues about networking as a way of finding work. Is this an important method? Is it effective? Is it more important in certain areas of work than others? (e.g. in manual work.) Do those with wider social networks have more success in finding work this way? Graham Gibbs Friday, April 28, 2000

46. Exercise 6 • Create and link a memo • Create a memo (Memo in Create ribbon). Write some content • Select a node and link this memo to it (use right mouse menu) • Create an annotation. Select some text in a source and use right mouse menu to annotate it. (Links:Annotation)

47. Descriptive vs Analytic/theoretical • Descriptive • Just what the people said • What happened • Their terms • Analytic • Use social science theory • Groups codes together • Use terms the respondents don’t or wouldn’t

48. Example of coding ‘Dancing’, ‘Indoor bowling’, ‘Dances at works club’, ‘Drive together’ Descriptive codes ‘Joint activities ceased’, ‘Joint activities continuing’ Categories ‘Loss of physical co-ordination’, ‘Togetherness’, ‘Doing for’, ‘Resignation’, ‘Core activity’ Analytic codes

49. Example showing coding marks

50. Line-by-line coding • Force analytic thinking whilst keeping you close to the data • Pay close attention to what the respondent is actually saying • Construct codes that reflect respondent's experience of the world