This document provides an overview of using NVivo software to conduct transparent qualitative data analysis. It discusses using a team or solo approach, documenting the analytic process through memos and a detailed codebook, coding interviews and other sources, running queries to identify relationships between codes and themes, and making the analysis iterative and supported by the source data. The goal is to facilitate a rigorous qualitative analysis process that meets standards for trustworthy qualitative research.

1. Behind the Wizard’s Curtain:
Using NVivo to Make Qualitative Analysis
More Transparent
Kari Greene, MPH
March 9, 2015
Program Design & Evaluation Services
Portland, Oregon

2. Qualitative Data Analysis (QDA) can seem
a bit like…

3. So let’s peel back that curtain & take a peek!

4. Overview
• Team vs. Solo QDA approach
• What sources inform inquiry
• Using memos to document process
• Building an explicit codebook
• Coding source data
• Use of queries
• Identifying themes

5. My History…

6. Focus on Analytic Procedures

7. Focus on Text-Based Analysis

8. A Whole Lotta Data…
• Conduct 25 interviews
– Roughly 20-minutes long
• Produces approximately…
– How many words?
– How many single-spaced pages?
65,000 words
150 pages

9. “Analysis brings moments of terror that there’s nothing there
and times of exhilaration from the clarity of discovering
ultimate truth. In between are long periods of hard work,
deep thinking, and weight-lifting volumes of material.”
Michael Quinn Patton
#omgMQP

10. Solo or Team Approach to Analysis

11. Key Terms & Concepts
• Sources
• Memos
• Codes (nodes)
• Codebook (coding tree, coding schema)
• Themes

12. Sources
Data sources
• Field notes
• Interviews
• Survey data
• Reports
• Material
informing
inquiry
• Internal &
external data
• Primary or
secondary
data

13. Memos
Memos
• Analytic
• Procedural
• Considered a
‘source’
• Ideas &
thoughts
generated
• Procedures
throughout
project
• Process
made explicit

14. Nodes
Nodes
• Contains coding
• Collection of
references to
concepts &
categories
• Able to code
participants
& places
• Relationships
• Matrices

15. Able to assign nodes to
different types of
sources
• Participants
• Places

16. Relationships between
concepts or codes
• Directionality
• Visualize thought
process

17. Node Matrices cross-
tabulates nodes
• Connections
between concepts
• Able to visualize

18. Building a Codebook
• Detailed description of each code
– Allows consistent use of codes throughout process
• Criteria of good codes
– Mutually exclusive codes
– Inclusion & exclusion criteria
– If code is abstract, provide examples of the boundaries
• Codebooks developed & refined throughout process
– Might have an initial broad codebook based on research questions
– Iterative process of coding source data & developing codebook
– Document significant changes in procedural & analytic memos

19. Example codebook report
• Explore / Report
• Import an NVivo
template

20. Coding Steps
• Read through all source data
– Create memos of what you notice AND your procedures
• Start open coding
– If part of a team, have everyone start with same section of source data
& compare open coding
– Document necessary changes to codebook
• Look for relationships between descriptive codes
• Time-intensive…multiple layers of coding the same data
– Importance of reflexivity & documentation
• May need additional data as ideas emerge

21. “Qualitative data analysis is generally a non-linear process
and often can be recursive. As you continue on collecting
information, you may notice new things and need to think
about them. As a result, you sometimes have to go back to
old data and analyze them again.”
Shahedul Huq Khandkar
Iterative Process

22. Queries =
“syntax”
Results of queries
• “Coding reports”
• Dynamic process

23. Identifying Themes
• Use analytic memos to add meaning to the data
• Keep research questions close at hand
– Easy to get lost in the data trenches
– Make them a source document (memo)
• Analytic themes must be supported by the data!!
– Cannot just reflect the mental model of the analyst
• Use of relationships & models to visualize findings
– Helpful when interrogating the data & in report-writing

24. “Don’t be afraid to be confused. Try to remain
permanently confused. Anything is possible. Stay open,
forever, so open it hurts, and then open up some more,
until the day you die, world without end, amen.”
George Saunders
Constantly Question…

25. So what I’m saying is…
• NVivo helps make the analytic process explicit
• Facilitates multiple ways to hit key criteria for
trustworthy qualitative inquiries (Lincoln & Guba, 1985)
1. Credibility
2. Transferability
3. Dependability
4. Confirmability

26. Thank you!
Kari Greene, MPH
971-673-0599
kari.greene@state.or.us