Info 272 qualitative research methods l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 16

Data Analysis: A Grounded Theory Approach PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

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.

Download Presentation

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.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

Info 272 qualitative research methods l.jpg

INFO 272. Qualitative Research Methods

Data Analysis: A Grounded Theory Approach

The iterative model l.jpg

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 l.jpg

From Analysis to Write up

5) draft writing

4) memo-writing


3) theoretical coding

2) focused coding

1) Initial coding

Grounded theory l.jpg

Grounded Theory

  • Analytic codes and categories arise out of the data

  • Simultaneous involvement in data collection and analysis (or very rapid iteration)

  • ‘Sampling’ aimed toward theory construction

  • Lit review after analysis

Coding l.jpg


  • …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 l.jpg

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)

What does coding do to data l.jpg

What does coding do to data?

  • Condenses

  • Disaggregates

Slide8 l.jpg

  • 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

Slide9 l.jpg

  • 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 l.jpg

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 l.jpg

After Coding: Some Heuristics

  • Sorting and Diagramming

    • Concept charting

    • Flow diagrams

  • Lofland and Lofland and Charmaz have many suggestions

Memo writing l.jpg


  • Transitioning between codes and write up

  • Could be blog entries

An example l.jpg

An Example

Slide14 l.jpg

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.

Slide15 l.jpg


Phone Gifting and Sharing

A coding exercise l.jpg

A Coding Exercise

  • Login