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Qualitative Research Coding and Interpretation

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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 Cancer Outcomes Research Program

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 Cancer Outcomes Research Program

4 Types of analysis Inductive: categories and themes emerge from data (grounded theory) Deductive: “fit” the data to categories and themes (a priori codes) Cancer Outcomes Research Program

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 Cancer Outcomes Research Program

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 Cancer Outcomes Research Program

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 Cancer Outcomes Research Program

8 Coding Cancer Outcomes Research Program

9 Levels of codes Themes Categories Codes
Cancer Outcomes Research Program

10 Coding processes Recording preliminary thoughts Establishing themes
Developing a coding framework Developing coding definitions Documenting memo-ing Cancer Outcomes Research Program

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 Cancer Outcomes Research Program

12 Coding exercise Cancer Outcomes Research Program


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