1. When All You Have Are Words: Qualitative Data Analysis Techniques
Tasha R. Wyatt, PhD
6/27/16
Educational Innovation Institute (EII)

3. Words… Reflect, Shape, Reveal
Beliefs
Background Experience
Conversations
Intentions
Actions
Perspective
Policies
Experience
Identity

4. Reveal what is both in plain sight and hidden.

5. Qualitative Analysis Evaluation Data Research Data What is working?
What is not working?
How can we make improvements?
How does this theory explain participants’ perspectives?
How do these perspectives contribute to the development of a new theory?

6. Inductive Inquiry* SIMPLE
Purpose: Allow research findings to emerge from the frequent, dominant or significant themes inherent in the raw data
SIMPLE
“Goal free” – describes actual
program effects,
not just planned effects
*Commonly used in health and social
science research and evaluation

7. How do you do it…?
3. Get to Know Your Data: Do a close reading of the text. Be familiar with the themes and events in the text.
4. Create Categories: Upper level categories
are derived from the evaluation aims.
Lower level categories are derived from
multiple readings.
Step by Step
5. Revision & Refinement of Categories: Within each category, look for subtopics, including contradictory points of view and new insights. Select quotes that convey core themes.
1. Clean your data!
2. Get Clarity: Data analysis is guided by evaluation objectives. These provide a focus or domain of relevance.

8. Can we trust your work? Peer Debriefing:
Discuss findings with peers. Does
this interpretation make sense? Do you
see alternative explanations?
Checks for Inter-Rater Reliability:
Independent Parallel Coding:
1st coder conducts analysis;
2nd coder repeats
Categories are compared
and merged
Member Checks:
Check with participants,
funding agencies, and providers who
comment on categories and
interpretations.
Clarity of Categories:
1st coder conducts analysis;
2nd coder is given raw data and
asked to use 1st coder’s categories

9. Writing Findings When writing up your findings:
Identify a label for the category
Include the authors’ description of the meaning of the category
Provide a quote from the raw text to elaborate on the category

10. Analyzing Data for Research
What does it mean to analyze content?
What are the various ways to analyze content?
How do you choose among different techniques?

11. What Does It Mean to Analyze Content?
Examination of large amounts of text
The purpose is to organize text in a meaningful way:
Types of text: Interviews, focus groups, meeting notes, open-response comments, lesson plans, syllabi, websites, evaluations, social media, student projects, etc.
Brings understanding to specific phenomena
Used to analyze explicit or inferred communication

12. “how” and “why”
How do medical students feel about moving to entrustable
professional activities (EPAs) at MCG?
How are EPAs shifting the way students prepare for residency?
Why are some medical students pushing back?

13. Technique 1: Emergent Content Analysis
Describe a phenomena where there is little published research
Hmm…what is going on here?
I see a pattern. What does it mean?
I’ve never seen anyone respond this way! I wonder why I am seeing this.
The goal is to develop a concept or a model

14. When you feel tipsy… let the data speak.

15. Step by Step Step 1: Immersion with all the data
Step 2: Code data directly from the text
Step 3: Sort into categories/meaningful clusters
Step 4: Define each category, subcategory, code
Step 5: Results contribute to knowledge development

16. Developing Trustworthiness
Build Trust with others
Peer debriefing
Discussion with a disinterested peer
Prolonged engagement
Adequate time with participants
Persistent observation
Themes, information begins to repeat itself
Triangulation
Linking data sources
Negative Case Analysis
Looking for examples of the opposite
Member checks
Verifying respondent answers

17. Technique 2: Directed Content Analysis
Develop, extend or validate theory and theoretical frameworks
Existing theory or research helps shape codes and relationship between codes
Step 1: Code using pre-determined codes. Data that doesn’t fit gets a new code
Step 2: Identify sub-codes for granulation

18. Apriori Coding… Shaping the data to fit your codes
You define the codes (from theory) and then overlay them on the data.

19. Developing Trustworthiness
Use an audit trail or audit process
An auditor should review definitions of codes before the study
Increases the accuracy of pre-determined codes

20. Technique 3: Summative Content Analysis
Quantify certain words or content in a text to understand their contextual factors
Objective: Explore usage and range of meaning in various contexts/speakers
Educators vs. Clinicians
Medical Education Textbooks
Online forums

21. When are these phrases used?
“Transitioning”
“Crossing Over”
“Meeting the Angels”
“Passing Away”
“Moving-On”
“Dying”
“Going to the Other Side”
“Going to the Great Choir in the Sky”

22. Summative Content Analysis
Step 1: Search for identified words
Step 2: Identify word frequency for given word
Step 3: Analyze the context and/or speaker for use
When is it used?
By whom?
What were they trying to say/not say?
What are the implications for this usage?

23. Developing Trustworthiness
You must demonstrate credibility
Use experts for definitions
The goal is to demonstrate internal consistency (show evidence is consistent with interpretation)
Member checking to check intended meaning

24. Choosing a Technique
You must first understand what you are trying to achieve.

25. Questions to Ask…
Am I trying to create a theoretical model to better understand this area?
Emergent Content Analysis
Do I want to see how this theory explains students actions/beliefs/identity?
Directed Content Analysis
Do I want to better understand the language currently being used to describe a shifting construct?
Summative Content Analysis

26. Don’t Analyze On Your Own
NVIVO
Atlas Ti
Dedoose
Excel

27. Qualitative Data Analysis Software
NVIVO
Dedoose
Atlas
Excel
You know where to find this!

28. Questions? Consider a consultation with the EII: edi@augusta.edu
Qualitative Data Analysis
Research Design
Advice on Manuscript Prep