What Can Qualitative Software D o for My Research? November 5, 2013 APHA Conference

1. What Can Qualitative Software Do for My Research? November 5, 2013 APHA Conference

2. Outline of Session • Overall Goal: Understand how qualitative data analysis software can improve the rigor of your public health research • Short introduction to qualitative analysis and computer-assisted qualitative data analysis software (CAQDAS) • Introduction to coding and coding exercise • NVivo demonstration and exploration

3. A Brief History of CAQDAS • 1981: Lyn and Tom Richards develop NUD*IST, the precursor to NVivo • 1994: Miles and Huberman discuss the use of software in qualitative analysis in their widely cited text • 2007: National Science Foundation publishes guidelines for the use of software in qualitative data analysis • 2013: NVivo, AtlasTi, EZ-Text, ANSWR, MaxQDA, HyperResearch and Dedoose are among the most commonly used tools today • Today at APHA 2013: Over 30 presentations mention using NVivo in their abstracts (see handout)

4. How can Software Help Improve the Rigor of Qualitative Analysis? Methodology X Y Z • Transparency • Saturation

5. Considerations in Choosing to Use Software • Sample size and multiplicity of data sources • Emphasis on replicability, rigor and transparency • Likelihood that there will be future opportunities to perform secondary analyses on the same dataset • Desire to publish in peer-reviewed journals • Interest in merging close-ended attributes into the qualitative dataset • Building capacity of analysis team including training time and costs • Budgetary parameters and software investment

6. Promoting Reliability and Validity in Analysis • Document the process of analysis including what is the statement of a respondent and what is interpretation by a researcher • Involve multiple analysts to check biases • Document in detail the process by which analytical themes or codes are developed • Train coders or analysts on coding structure and create well-defined themes. Refine again and again. • In analysis, check inter-rater reliability • Develop conventions for transcribing data so that transcripts are comparable across data source • Develop saturation guidelines

7. Future Directions in Qualitative Analysis • Mixed methods tools, such as the capacity to work with datasets containing both fixed response and open-ended material • Web-based data, including social media and online discussion boards • Capacity for larger samples, especially large quantities of text (qualitative studies are no longer small) • More tools for comparing coding by researcher, theme, and participant group

8. Qualitative NVivo Quantitative Import/Export from Excel, text and database files Open-Ended and Fixed Response Questions Within and Between Group Analysis of Coding Text Analysis Kappa Coefficient Cluster Analysis of Word and Coding Similarity Coding of Text Audio,Video and Image Data Open-Ended Survey and Interview Responses Transcribing tools for Audio and Video A Future Look at Mixed Methods with NVivo Descriptive Statistics Inferential Statistics Meta-Analysis

9. Coding Exercise

10. Brief Coding Exercise: Purpose • We will code a short transcript manually • Given the short timeframe we will not do this thoroughly • We will talk about what we found and how we might begin to refine our codes • We will discuss how we would use qualitative software to accomplish the same steps

11. Coding Any researcher who wishes to become proficient at doing qualitative analysis must learn to code well and easily. The excellence of the research rests in large part on the excellence of the coding. (Anselm L. Strauss, Qualitative Analysis for Social Scientists, 1987, p. 27)

12. What is Coding? • Codes are short words or phrases that symbolize the essence of a piece of text, visual image, or other qualitative data. • Codes reduce a large quantity of data into more manageable “themes.” • Interpret qualitative data into meaningful themes (meaningful depends on the lens of the analyst)

13. EXAMPLE: Focus Group on Treating Chronic Fatigue Patients Physician Participant Code Trends in Diagnoses OR Physician training OR Chronic fatigue syndrome familiarity “There are also trends over times. When I was in training, everybody who we now consider chronic fatigue or even chronic fibromyalgia was largely looked into a group that they called the hypochondriacal patients. Now you hardly ever hear the diagnosis hypochondriasis anymore.”

14. Coding process • Initial codes will be defined, redefined, collapsed as more and more data are coded • Analysts will develop definitions, inclusion and exclusion criteria for each code

15. Analysis of Codes/Themes • Codes/themes are analyzed for patterns, e.g. • frequency, • similarity and differences across respondent types, • meaning, • sequence, • associations with other codes, • causation, etc.

16. Brief Coding Exercise - Instructions • Read the interview with Thomas – 10 minutes • Remember the purpose of the research. (Description in packet.) • Look for themes. Put parentheses around the text and write the word/phrase that summarizes the text next to it. • Note questions or ideas that occur to you as you read the interview. • Report out and discussion – 10 minutes

17. Brief Coding Exercise – Themes What themes did you come up with?

18. Brief Coding Exercise – Discussion • What codes are similar to each other? • How will we capture the changing definitions over time? • How will we merge codes? • How will we assess inter-rater reliability? • What questions or comments occurred to you as you read the interview?

19. Brief Coding Exercise – Summary • What works manually with a small amount of qualitative research becomes more complicated as the number of qualitative sources increase • Defining, redefining, merging, and separating codes is easier to do and easier to keep track of with qualitative software • Documenting the analysis process systematically is a benefit of the software; replicability is possible

20. NVivo Demonstration and Exploration

21. Let’s explore NVivo in practice

22. Systematic, Rigorous, Quick • Increase accessibility of data and transparency of analysis • Node content, memos, annotations, coding stripes, event log • Identify and test ideas about emerging patterns and themes • Text Search Query, Matrix Coding Query • Utilize open-ended text and non-text data • Transcripts, audio, video, pictures, survey and other categorical data, social media • Generate output for reporting • Query results, node exports, visualizations • Support team-based analysis • View team members’ coding, calculate Kappa coefficient

23. Utilizing Non-text Data Classification (First Cell) Attributes (First Row) Nodes (First Column)

24. Questions and Comments?

25. Contact Information • Lisa LeRoy, MBA, PhD Lisa_leroy@abtassoc.com 617-349-2723 • Ilana Ron, MSc Ilana_ron@abtassoc.com 301-347-5339 • Cynthia Jacobs, EdD C.Jacobs@qsrinternational.com 617-491-1850