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Adding and finding meaning in case-by-case network graphs of interviews

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This is a presentation that I gave at the e-Humanities Group on 31 October 2013 in Amsterdam.

For the ACUMEN project, I collected career data from and conducted interviews with about 40 university-based researchers and 10 deans, department heads and human resources managers. Career data typically comes in the form of CVs, which are suitable for storing and coding in relational databases. Doing interviews results in notes, transcriptions and coding added to the transcriptions. This is usually done with coding software such as NVIVO, Atlas or TAMSAnalyzer. Database software does not produce network graphs. Coding software is good at producing network graphs, but bad at dealing with relational data. The problem then is how to combine the two. For the ACUMEN project, I explored a few possibilities. I will present one of these and evaluate its use as a tool for exploration and analysis.

http://www.research-acumen.eu/

Published in: Design
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Adding and finding meaning in case-by-case network graphs of interviews

  1. 1. Adding and finding meaning in case-by-case network-graphs of interviews Frank van der Most ! e-Humanities group and DANS ! New Trends in e-Humanities, 31 October 2013 1
  2. 2. What does it mean? !2
  3. 3. A practical research problem ✦ ± 50 interviews •Fairly structured •transcribed and coded ✦ Interested in a sub-set (27), on a sub-topic, viz. informal evaluations ✦ How to get an overview? ✦ Okay, let’s try visualizing instead of reading !3
  4. 4. The raw material ✦ Interviewee info: date of birth, date of PhD, CV-events ✦ 3 Most important developments + 3 most influential evaluations •Event code + Event-code group •Info about event: period, country, affiliation summary ✦ Coded interview transcriptions •Importance •Micro-stories !4
  5. 5. Tuesday, 13:47 6 Interviewees Importance
  6. 6. Tuesday 14:40 10 Interviewees Importance Micro-stories
  7. 7. Tuesday 14:40
  8. 8. Tuesday 16:33 20 Interviewees Importance
  9. 9. Tuesday 16:33
  10. 10. Does a table work better? Importance count importance>long_term_stability 8 importance>family 6 importance>doing_research 6 importance>academia_or_not_question 5 importance>enjoying_one_s_work 5 importance>having_the_necessary_skills_or_qualifications 4 importance>mobility 4 importance>personal_achievement 4 importance>societal_impact 4 importance>academic_standing 4 importance>contacts_or_network 3 Yes for quantitative overview, still no for qualitative
  11. 11. Wednesday, 16:19
  12. 12. Thursday, 11:45
  13. 13. Thursday, 16:30
  14. 14. Thursday, 16:30
  15. 15. Thursday, 16:30
  16. 16. Thursday, 16:30
  17. 17. Thursday, 16:30
  18. 18. Thursday, 16:30
  19. 19. Saturday, 11:53
  20. 20. Sunday, 19:23
  21. 21. Monday, 20:10
  22. 22. Recap, what can we see and what it means Tim e/ sp ac e it y ar co n ne cti o E nt ve ns cror mi ce o r tan o ities Imp ilar sim t or y s il sim pe -ty
  23. 23. Dominant shape
  24. 24. What we can not see (but may read) ✦ Time dimension ✦ interviewee info and ✦ Geography ✦ Relations between ‘importance’ and ‘microstories’ !41
  25. 25. Relations between ‘importance’ and ‘micro-stories
  26. 26. Thank you for your attention io t s e u Q ? s n Com m en t s ? www.frankvandermost.nl !44

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