Qualitative data analysis

1. Qualitative data analysis

2. Principles of qualitative data analysis • Important for researchers to recognise and account for own perspective • Respondent validation • Seek alternative explanations • Work closely with same-language key informant familiar with the languages and perspectives of both researchers and participants

3. Principles of qualitative data analysis • Context is critical i.e. physical, historical, social, political, organisational, individual context Dependence/interdependence • Identify convergence / divergence of views and how contextual factors may influence the differences

4. Principles of qualitative data analysis • Role of theory guides approach to analysis • Established conceptual framework – predetermined categories according to research questions • Grounded theory – interrogate the data for emergent themes

5. Principles of qualitative data analysis • Pay attention to deviant cases / exceptions • Gives a voice to minorities • Yield new insights • Lead to further inquiry

6. Principles of qualitative data analysis • Data analysis is a non-linear / iterative process • Numerous rounds of questioning, reflecting, rephrasing, analysing, theorising, verifying after each observation, interview, or Focus Group Discussion

7. Stages in qualitative data analysis • Interrelated rather than sequential • During data collection • Reading – data immersion – reading and re-reading • Coding – listen to the data for emerging themes and begin to attach labels or codes to the texts that represent the themes

8. Stages in qualitative data analysis • After data collection • Displaying – the themes (all information) • Developing hypotheses, questioning and verification • Reducing – from the displayed data identify the main points

9. Stages in qualitative data analysis • Interpretation (2 levels) • At all stages – searching for core meanings of thoughts, feelings, and behaviours described • Overall interpretation • Identify how themes relate to each other • Explain how study questions are answered • Explain what the findings mean beyond the context of your study

10. Processes in qualitative data analysis • Reading / Data immersion • Read for content • Are you obtaining the types of information you intended to collect • Identify emergent themes and develop tentative explanations • Note (new / surprising) topics that need to be explored in further fieldwork

11. Processes in qualitative data analysis Reading / Data immersion • Read noting the quality of the data • Have you obtained superficial or rich and deep responses • How vivid and detailed are the descriptions of observations • Is there sufficient contextual detail • Problems in the quality of the data require a review of: • How you are asking questions (neutral or leading) • The venue • The composition of the groups • The style and characteristics of the interviewer • How soon after the field activity are notes recorded • Develop a system to identify problems in the data (audit trail)

12. Processes in qualitative data analysis Reading / Data immersion • Read identifying patterns • After identifying themes, examine how these are patterned • Do the themes occur in all or some of the data • Are their relationships between themes • Are there contradictory responses • Are there gaps in understanding – these require further exploration

13. Processes in qualitative data analysis • Coding – Identifying emerging themes • Code the themes that you have identified • No standard rules of how to code • Researchers differ on how to derive codes, when to start and stop, and on the level of detail required • Record coding decisions • Usually - insert codes / labels into the margins • Use words or parts of words to flag ideas you find in the transcript • Identify sub-themes and explore them in greater depth

14. Processes in qualitative data analysis Coding – Identifying emerging themes • Codes / labels • Emergent codes • Closely match the language and ideas in the textual data • ‘Borrowed’ codes • Represent more abstract concepts in the field of study • Understood by a wider audience • Insert notes during the coding process • Explanatory notes, questions • Give consideration to the words that you will use as codes / labels – must capture meaning and lead to explanations • Flexible coding scheme – record codes, definitions, and revisions

15. Processes in qualitative data analysis Coding – Identifying emerging themes • Code continuously as data collection proceeds • Imposes a systematic approach • Helps to identify gaps or questions while it is possible to return for more data • Reveals early biases • Helps to re-define concepts

16. Processes in qualitative data analysis Coding – Identifying emerging themes • Building theme related files • Conduct a coding sort • Cut and paste together into one file similarly coded blocks of text • NB identifiers that help you to identify the original source See example on Clandestine Microbicide Use

17. Processes in qualitative data analysis • Displaying data i.e. laying out or taking an inventory of what data you have related to a theme • Conduct quantitative and qualitative analysis • Capture the variation or richness of each theme • Note differences between individuals and sub-groups • Organise into sub-themes • Return to the data and examine evidence that supports each sub-theme • Note intensity/emphasis; first- or second-hand experiences; identify different contexts within which the phenomenon occurs

18. Processes in qualitative data analysis • Developing hypotheses, questioning and verification • Extract meaning from the data • Do the categories developed make sense? • What pieces of information contradict my emerging ideas? • What pieces of information are missing or underdeveloped? • What other opinions should be taken into account? • How do my own biases influence the data collection and analysis process?

19. Processes in qualitative data analysis • Data reduction i.e.distill the information to make visible the most essential concepts and relationships • Get an overall sense of the data • Distinguish primary/main and secondary/sub- themes • Separate essential from non-essential data • Use visual devices – e.g. matrices, diagrams

20. Processes in qualitative data analysis • Interpretation i.e. identifying the core meaning of the data, remaining faithful to to the perspectives of the study participants but with wider social and theoretical relevance • Credibility of attributed meaning • Consistent with data collected • Verified with respondents • Present multiple perspectives (convergent and divergent views) • Did you go beyond what you expected to find?

21. Processes in qualitative data analysis Interpretation • Dependability • Can findings be replicated? • Multiple analysts • Confirmability • Audit trail • Permits external review of analysis decisions • Transferability • Apply lessons learned in one context to another • Support, refine, limit the generalisability of, or propose an alternative model or theory