1 / 17

Data Analysis: A Grounded Theory Approach

INFO 272. Qualitative Research Methods. Data Analysis: A Grounded Theory Approach. Admin. Syllabus changed around a bit Guest speakers scheduled. The Iterative Model. 1) research topic/questions. 2) ‘corpus construction’. 3) data gathering. Field work. 4) analysis. 4) more analysis.

corin
Télécharger la présentation

Data Analysis: A Grounded Theory Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. INFO 272. Qualitative Research Methods Data Analysis: A Grounded Theory Approach

  2. Admin • Syllabus changed around a bit • Guest speakers scheduled

  3. The Iterative Model 1) research topic/questions 2) ‘corpus construction’ 3) data gathering Field work 4) analysis 4) more analysis Desk work 5) write-up

  4. From Analysis to Write up 5) draft writing 4) memo-writing Granularity 3) theoretical coding 2) focused coding 1) Initial coding

  5. Grounded Theory • Constructing analytic codes and categories from data • Simultaneous involvement in data collection and analysis • ‘Sampling’ aimed toward theory construction • Lit review after analysis

  6. Grounded Theory, e.g.

  7. Coding… • …is attaching labels to segments of data that depict what each segment is about • …is the bones of your analysis • …forces you to interact with your data (again and again)

  8. Coding: Key concepts • Granularity varies • Word-by-word, line-by-line, incident-to-incident • observational data vs. interviews • Ideas, categories, concepts must ‘earn their way’ into your analysis • ‘in vivo’ codes (attention to language) • Constant comparative method • Are provisional!

  9. Warning! • Be careful with the language of intention, motivation, strategy • Don’t impute • Treat social reality as what is apparent, presented to you (not underlying, secret motives)

  10. What does coding do to data? • Condenses • Disaggregates

  11. Remain open • Stay close to the data • Keep codes simple and precise • Construct short codes • Preserve actions [gerunds – i.e. shifting, interpreting, avoiding, predicting] • Compare data with data • Move quickly through the data 1) Initial coding

  12. Take most frequent codes from the initial coding • tying emerging concepts to the data (verification process) 2) focused coding 1) Initial coding

  13. After Coding: Some Heuristics • Sorting and Diagramming • Concept charting • Flow diagrams • Lofland and Lofland and Charmaz have many suggestions

  14. Memo-Writing • Transitioning between codes and write up • Could be blog entries

  15. Timesavers and Shortcuts • Moving along quickly to ‘focused coding’ • Do ‘initial’ coding on a selection of the data (the early data, the most rich material) • Software (for searching especially) – NVivo or even MS OneNote

  16. A Coding Exercise

More Related