1. Note Taking and Analyzing Qualitative Data
Roxanne Ezzet-Lofstrom
URBP 298A
San Jose State University
11/28/2018

2. Overview Goals of note taking How to take notes
What to do with your notes
Data management
Analyzing qualitative data
Questions about your projects
11/28/2018

3. Goals of Note Taking Adapted from material provided by Prof
Goals of Note Taking Adapted from material provided by Prof. Asha Agrawal
What MIGHT your goals be?
Possible goals
Retrieve details from original documents
Compile information if you cannot go back to originals
Keep track of what your read; don’t have to hunt for it again
Help understand what you’re reading
Help to remember what to read again later
Help you remember what you read
11/28/2018

4. How to Take Notes Various source material
Detailed evidence to support statements
Note taking as part of a process
Use only a portion of notes
Compress the information
Highlight key points
11/28/2018

5. How to Take Notes, con’t Be selective Problems with too many notes
Transcribing too much of original
Being unselective in topic choice
Solution to too many notes
Avoid being descriptive
Think more….write less
Be very selective
11/28/2018

6. Note Taking Examples Interviews
Practice interview and note taking before conducting “real” ones
Record interview if possible
Immediately after the interview, review notes and fill in gaps
Later that same day, review/type/transcribe the notes from the interview
11/28/2018

7. Note Taking Examples, con’t
Documents (e.g., zoning codes, maps, general plans) or Observations (e.g., activity in parks or plazas)
List key themes to look for before reviewing documents
Put “data” into a grid, matrix, spreadsheet or systematic file
Examples later in today’s lecture
11/28/2018

8. What to do with all that data
Data Management
Disguise participants (as appropriate)
Create filing system (i.e., excel file, database) & be consistent
Qualitative Data Analyses (QDA)
Choices & Decisions
Iterative process: reading, describing & interpreting
11/28/2018

9. Analyzing Qualitative Data
Filtering data
Start with raw data (e.g., maps, field notes, interview transcripts)
Codes, themes, categories
Log decisions in a journal
Various approaches to present/write up the data (discuss in a few minutes)
11/28/2018

10. Coding Identifying and labeling codes Begin with open codes
Write in margins
After 3-4 transcripts, compile a start-list of codes
Trim back code list
Labels should be brief but descriptive
Keep a log of decisions
11/28/2018

11. Categories & Themes Producing categories and themes
Cut & paste coded segments
Can use QDA software
ATLAS/ti
NVivo
NUD*IST
Hyper RESEARCH
The Ethnograph
CDC EZ-Text (free download)
11/28/2018

12. Qualitative Data Presentation
Data Displays (e.g., matrix)
Case Summaries
11/28/2018

13. Example, housing policies
Want to determine affordable housing policies in major cities in California
Examine Los Angeles, San Francisco, San Jose, San Diego
Look for:
Inclusionary zoning
Density bonuses
Senior housing
other
11/28/2018

14. Example, inclusionary housing policies
**Fictitious results
Los Angeles – in lieu fee, off-site construction, land dedication, density bonus
San Francisco - in lieu fee, off-site construction, flex design standards, no density bonus
San Jose –density bonus
San Diego – in lieu fee, off-site construction, credit transfer, density bonus
11/28/2018

15. Example, density bonuses
**Fictitious results
Los Angeles – density bonus given for developments with 50+ units
San Francisco – none
San Jose – density bonus given for developments with 25+ units
San Diego – density bonus given for developments with 20+ units
11/28/2018

16. Housing policies, matrixLA SF SJ SD
Inclusionary zoning X
Density bonus
In lieu fees
Off site construction
11/28/2018

17. Congestion Management, matrix
Oakland
Orange County
Dallas
San Diego
HOV lanes
3 people
2 people
HOT Lanes no
Yes-2 people
Yes – 2 people
Yes – 1 person
Ramp metering
yes
Reversible HOV lanes
11/28/2018

18. Housing policies, case summary
Los Angeles uses many inclusionary zoning tactics such as density bonuses, in lieu fees and off site construction to meet the needs…..
San Francisco does not have density bonuses but it does use…..
San Jose only offers density bonuses as its approach to provide inclusionary housing….
11/28/2018

19. In Class Exercise
Survey question: What are the main problems with your local park?
Responses: there are too many muggings, cars drive too fast, there are no lights so the park is very dark at night, there is a lot of criminal activity around the park
11/28/2018

20. Example, coding responses
category
theme
Mugging
Cars go too fast
Robberies
Can’t see at night
Feel uncomfortable
No lights
Too many cars
Crime
Very dark
11/28/2018

21. Example, creating categories
response
category
theme
Mugging
crime
Cars go too fast
traffic
Robberies
Can’t see at night
lighting
Feel uncomfortable
No lights
Too many cars
Crime
Very dark
11/28/2018

22. Example, creating themes
response
category
theme
Mugging
crime
safety
Cars go too fast
traffic
Robberies
Can’t see at night
lighting
Feel uncomfortable
No lights
Too many cars
Crime
Very dark
11/28/2018

23. References
Coffey, A., & Atkinson, P. (1996). Making Sense of Qualitative Data. Thousand Oaks, CA: Sage.
Padgett, D. (2008). Qualitative Methods in Social Work. Thousand Oaks, CA: Sage.
Royse, D. (2008). Research Methods in Social Work, Belmont, CA: Thomson Brooks/Cole.
Silverman, D. (2005). Doing Qualitative Research: A Practical Handbook, Thousand Oaks, CA: Sage.
Wolcott, H.F. (1990). Writing Up Qualitative Research, Newbury Park, CA: Sage.
Wolcott, H.F. (1994). Transforming Qualitative Data, Thousand Oaks, CA: Sage.
11/28/2018