info 272 qualitative research methods n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Data Analysis: A Grounded Theory Approach PowerPoint Presentation
Download Presentation
Data Analysis: A Grounded Theory Approach

Loading in 2 Seconds...

play fullscreen
1 / 17

Data Analysis: A Grounded Theory Approach - PowerPoint PPT Presentation


  • 372 Views
  • Uploaded on

INFO 272. Qualitative Research Methods. Data Analysis: A Grounded Theory Approach. 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. From Analysis to Write up.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Data Analysis: A Grounded Theory Approach' - yardley


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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
the iterative model
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

from analysis to write up
From Analysis to Write up

5) draft writing

4) memo-writing

Granularity

3) theoretical coding

2) focused coding

1) Initial coding

grounded theory
Grounded Theory
  • Constructing analytic codes and categories from data
  • Simultaneous involvement in data collection and analysis (or very rapid iteration)
  • ‘Sampling’ aimed toward theory construction
  • Lit review after analysis
coding
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)
coding key concepts
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 (close attention to language)
  • Constant comparative method
  • Are provisional! (code quickly)
warning
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)
what does coding do to data
What does coding do to data?
  • Condenses
  • Disaggregates
slide9

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

slide10

Take most frequent and analytically interesting codes from the initial coding

  • Tying emerging concepts to the data (verification process)

2) focused coding

1) Initial coding

timesavers and shortcuts
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
after coding some heuristics
After Coding: Some Heuristics
  • Sorting and Diagramming
    • Concept charting
    • Flow diagrams
  • Lofland and Lofland and Charmaz have many suggestions
memo writing
Memo-Writing
  • Transitioning between codes and write up
  • Could be blog entries
slide15

Henry:If your original idea was – if the target group was women, the poor women, why weren’t these phones strictly earmarked for them?

Jenna:I don’t know cause there was someone I talked to who was talking about how great it was that 80% of village phone operators are women…

Henry: The operators were women but they were not the owners.

Henry:Here in Uganda the owners most of the owners were men actually 90% of the phones were owned by men…

Jenna:So they were making themselves small amounts, but big profits were going to the owners?...[women] were not the entrepreneurs?

Julius:And even men, men who bought them, women who had them as closest to being owners it’s their husbands who facilitated them.

slide16

Diagramming

Phone Gifting and Sharing