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Analyzing Interview Data

Analyzing Interview Data. Elizabeth Boyd, Ph.D. EPI 240 April 20, 2006. After the Interview. You’ve interviewed 10 (or 20, or 30, or 100) people, now what? Transcription Coding Analysis. Transcription. Written representation of the interview Types of transcription:

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Analyzing Interview Data

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  1. Analyzing Interview Data Elizabeth Boyd, Ph.D. EPI 240 April 20, 2006

  2. After the Interview • You’ve interviewed 10 (or 20, or 30, or 100) people, now what? • Transcription • Coding • Analysis

  3. Transcription • Written representation of the interview • Types of transcription: • “Cleansed” transcript • “Just the words” • “Jeffersonian” transcript

  4. The “cleansed” transcript • Dr. E: I’m deputy editor of Annals of Internal Medicine. I was associate editor from 1978 to 1999, and I was deputy editor from 1999 to 2003. My sub-specialty is pulmonary disease which I practice every day at the University of Pennsylvania. Most of the editors at Annals do practice, though not as extensively as I do. …

  5. “Just the words” • IR: So today is March seventh. I’m at Annals of Internal Medicine and I’ll be interviewing Dr. P.E. And for the record can you state your name and position? • DrE: It’s P.E. I’m deputy editor of Annals of Internal Medicine. • IR: Okay. And how long have you been working at Annals? • DrE: Since 1978. It’s a long time. I was associate editor from 1978 to 1999 and I’ve been deputy editor from 1999 to 2003.

  6. “Jeffersonian” transcript • IR: So: today is March seventh, I’m at Annals of Internal Medicine and I’ll be interviewing doctor Pete Ernest. (0.4) A::nd um for the record can you state your name and position? • IE: It’s Pete Ernest, I’m deputy editor of Annals of Internal Medicine. • IR: Okay. And how long have you been working at Annals? • (0.4) • IE: Since nineteen seventy eight. It’s a lo::ng time. I was uh:: associate editor from nineteen seventy eight t nineteen ninety ni:ne, …

  7. Which transcription method to use? • Speed versus detail and accuracy • What are you most interested in learning -- • Content? • Narrative? • Interaction?/Context?

  8. Coding • Goal: To link specific quotes to analytic concepts and categories • Some categories precede interviews • Based on assumptions, literature • Others emerge from data itself • Unexpected observations, new insights • Coding categories evolve through your interactions with the data

  9. Coding: First steps • Codes should stick closely to the data • Preserve words/phrases of Ies • Preserve events • Portray viewpoints • Suggest contexts

  10. Coding: Initial phase • Naming each line, segment • Open-mind; avoid preconceptions • Look for ACTIONS as well as topics • Allow new ideas to emerge • Codes are provisional • Use gerunds to characterize -- • “reporting” “naming” “complaining” “mourning” etc. • “In vivo” codes: retaining the IEs words, phrases

  11. How to avoid imposing preconceptions on data • Achieve intimate knowledge of your data • Understand how your respondents understand • Don’t take for granted that you know/understand what your repsondents are telling you • Specify how your concepts help you understand your data • Can you adequately explain your data without extant concepts? • If so, what do they add?

  12. How to avoid imposing preconceptions on data • If extant concepts do not add substance to your analysis, are not integral to your understanding, do not use them for the sake of using them. • Recognize and reflect upon your own preconceived categories • Whenever you want to say, “It is x,” ask “how, why is it so?”

  13. Beginning analysis • After initial coding, your analysis becomes selective, directed, and more conceptually motivated • Select analytic categories of interest • Compare across interviewees and observations • Elaborate and expand each category • Find boundary cases, deviant cases, typical cases

  14. Analysis • Final stage involves explaining how codes/categories may relate to one another -- formulating hypotheses, integrating into theory • Looking for causes, contingencies, consequences, covariances, and conditions

  15. A note on causality • Causal relationships in qualitative data: • A description of a visualizable sequence of events, with each event clearly leading to the next • Description must be more than plausible -- it must be thorough and systematic

  16. References • Charmaz, Kathy. Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage Publications. 2006.

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