1. Qualitative Research Coding and Interpretation
Cancer Outcomes Research Program
Qualitative Research Coding and Interpretation
Lara Traeger, PhD
Co-presenter: Emily Wright, BA
Methods in Supportive Oncology Research

2. Quality and credibility
Credibility: ‘truth’ of the findings
Confirmability: confidence that the conclusions are fair/accurate
Transferability: extent to which findings may relate to other settings
Dependability: extent to which findings are reproducible
Credibility (vs. validity) –Verification with participant; theoretical verification – compare results to other studies
Confirmability -- seek out negative evidence, test rival explanations
Transferability (vs. generalizability) –can we see similarities in our findings that relate to other settings
Dependability (vs. reliability) –extent to which research would produce similar/consistent findings if carried out as described; keep record of research process
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3. Strategies and techniques for enhancing rigor
Respondent validation
Attention to reflexivity
Triangulation
Attention to rival hypotheses and cases that deviate from theory
Supervision and skeptical peer review of analysis
Respondent validation – researcher shares theories/interpretations with participants at end of interview or at end of data collection
Reflexivity – researcher reflects on ability to be unbiased and how this might affect the research; idea – reflective journaling, audit trail
Triangulation --multiple methods to gather and confirm data; can triangulate by investigator, theory, method, or data (time space source)
Frankel & Devers, Education & Health, 2000
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4. Types of analysis
Inductive: categories and themes emerge from data (grounded theory)
Deductive: “fit” the data to categories and themes (a priori codes)
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5. Characteristics of analysis
Systematic and verifiable
Theory driven
Iterative process
Flexible to alternate views
Includes a system to handle incongruence between coders
Audit Trail: clear account of research process and decisions so that reader can judge the dependability of the study
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6. Analysis occurs throughout study
During interviewing and coding:
Note impressions, ideas and early interpretations
Begin to identify data characteristics and differences
Discuss with multidisciplinary team members
As themes emerge:
Consider how the data map onto – or challenge – existing concepts
Consider how new concepts may emerge
Consider how different concepts connect – relationally, causally
As you go deeper:
In addition to describing phenomena, look for reasons that phenomena emerged
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7. Keeping track of the analysis process
Theories that influenced the data analysis
How data were organized
Multiple coder strategy
How you conducted data reduction
How conclusions were drawn & verified
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8. Coding
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9. Levels of codes Themes Categories Codes
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10. Coding processes Recording preliminary thoughts Establishing themes
Developing a coding framework
Developing coding definitions
Documenting memo-ing
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11. Coding: manually or via software
Considerations:
Formatting and storing source documents
Setting up a coding scheme
Coding
Checking coding consistency
Maintaining audit trail
Quantity versus meaning
Budget
Advantages of software:
Reducing time needed for manual handling tasks
Increased flexibility in handling large amounts of data
More rigorous analysis of data
More visible data analysis audit trail
Disadvantages of software:
Focus on quantity vs. meaning
Data can become too reduced/too much focus on coding & retrieval
Time spent learning use
Pressures to use/commercialism
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12. Coding exercise
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