Qualitative research analysis

1. Qualitative research analysis M.H. Forouzanfar

2. A dialectic process Process is dialectic not linear: Learn something Collect data Make sense Go back and get new experiences Refine interpretation (more analysis) And �

3. Steps of QA Is not a cookbook fashion Intellectual craftsmanship Should be done artfully, even playfully Translation of field work into a text (communicating understanding to reader)

4. Steps of QA, data preparation Data preparation Transcribing data Type tape Who will transcribe Transcribe all data? Organize field notes Research question and theoretical framework Positivist view Simple translation Interpretive view point Not transparent Importance of researcher�s point of view Multiple levels of meaning Feminist view point Listen to the data, special moments:� you know what I mean�

5. Data exploration Read and think about texts Mark up and highlight important sentences Write down ideas Emphasize on description Code data, don�t wait for all data

6. Coding and analyzing Open coding: finding segments in textual data and giving a label (code) 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?"

7. Example

8. Example I don't think that the ideal woman has to look like anything personally. I think the ideal woman has personality and character, its how you act. My looks don't bother me, it's just my personality. My personality. I wanna have a good personality and have people like me, if they don't like me for my personality, or just because of my looks, then they must be missing out on something. Um, when you have it [self-esteem] so much that you don't care what people .. think about you. I man, I flaunt my self-esteem, not like 'Oh yeah, dahdadada: I just sit up real straight and that shows self-esteem right there. I'm a woman, I'll wear stuff to school that's like . . . wacked. I have earrings that are about this big, and that shows my self-esteem, I don't care what you say. about them, , , Oh well, that's what I think, I don't care, I don't fit in anywhere anyway, I'm my own self so why can't I act like that, why can't I dress like that? Ideal woman Importance of personality Physical appearance is secondary Importance of personality Importance of personality Missing out on noticing personality Self-esteem Don't: care what others say Flaunting myself Sits straight Wears what she wants Wears big earrings Doesn't care what others say Internal self-assessment: own person Internal self-assessment: wears what she wants

9. Coding

10. Memo writing From level of code to the level of category Descriptive codes within one�s data and hoping to generate a set of key concepts (categories) Reading then marking then coding (open coding) segments then immersion and induction then look for common ways or patterns

11. Memo writing "A new idea for a code. Just a quick hunch. Integrative discussion As a dialogue amongst researchers. To question the quality of the data. To question the original analytic framework. What is puzzling or surprising about a case? As alternative hypotheses to another memo. If you have no clear idea but are struggling to find one. To raise a general theme or metaphor.

12. Memoing Similar and different ways to talk about an idea

14. Interpretation Data memo: Integrates the theme with data and literature Look like a paper Positivist and interpretive framework Issue of interpretation and storytelling

15. Data reduction and collapse

16. Validity and reliability of interpretation Validity as craftsmanship Are you telling a convincing story? Try theorizing from your data interpretations. Have you reached your findings with integrity-have you checked your procedures? Look for negative cases. Communicative validity Make your interpretations available for discussion (agreement and debate) among "legitimate knowers" Pragmatic validity How do your findings impact those who participated in the research, How do your findings impact the wider social context in which the research occurred? Reliability Is there "internal consistency" (Neuman, 2003)? Does the data add up?

17. Computer assisted software for QDA Generic software: Word processors Text retrievers Text-base managers Specific softwares: Code and retrieve program Code-based theory-building programs Conceptual network building programs Textual mapping software

18. Uses of computer software Making notes in the field Writing up or transcribing field notes Editing: correcting, extending, or revising field notes Coding: attaching keywords or tags to segments of text to permit later retrieval Storage: keeping text in an organized database Search and retrieval: locating relevant segments of text and making them available for inspection Data "linking": connecting relevant data segments to each other, forming categories, clusters, or networks of information Memoing: writing reflective commentaries on some aspect of the data as a basis for deeper analysis Content analysis: counting frequencies, sequence, or locations of words and phrases Data display: placing selected or reduced data in a condensed, organized format, such as a matrix or network, for inspection Conclusion-drawing and verification: aiding the analyst to interpret displayed data and to test or confirm findings Theory building: developing systematic, conceptually coherent explanations of findings; testing hypotheses Graphic mapping: creating diagrams that depict findings or theories Preparing interim and final reports

19. Fears and critics Computer programs will separate the qualitative researcher from the creative process The line between quantitative and qualitative analysis will be blurred by imposing the logic of survey research onto qualitative research and by sacrificing in-depth analysis for a larger sample. Computer software programs will determine the types of questions asked and specific data analysis plans Computer programs for analyzing qualitative data require the researcher to be more explicit in the procedures and analytical processes they went through to produce their data and their interpretations. Loss of confidentiality through the use of multimedia data.

20. Writing up of qualitative data Realist tales Traditional writing Take the form of a scholarly publication Presentation of respondents is a �true� reflection Detail about authors are absent Concrete details of what, how often, what order and whom Identify typical activities members point of view and interpretation of events Meaning of setting from members� perspective Non-realist tales Reflexive style