Transcript Qualitative data analysis - University of KwaZulu

Qualitative data analysis
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
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
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
Principles of qualitative data analysis
• Pay attention to deviant cases /
exceptions
– Gives a voice to minorities
– Yield new insights
– Lead to further inquiry
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
Stages in qualitative data analysis
• Interrelated rather than sequential
• During data collection
– Reading – data immersion – reading and rereading
– Coding – listen to the data for emerging themes
and begin to attach labels or codes to the texts
that represent the themes
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
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
Processes in qualitative data analysis
1. Reading / Data immersion
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Read for content
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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
Processes in qualitative data analysis
Reading / Data immersion
– Read noting the quality of the data
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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:
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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)
Processes in qualitative data analysis
Reading / Data immersion
– Read identifying patterns
• After identifying themes, examine how these are
patterned
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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
Processes in qualitative data analysis
2. Coding – Identifying emerging
themes
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Code the themes that you have identified
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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
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
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
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
Processes in qualitative data analysis
3. Displaying data
i.e. laying out or taking an inventory of what data you have
related to a theme
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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
Processes in qualitative data analysis
4. 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?
Processes in qualitative data analysis
5. Data reduction
i.e.distill the information to make visible the most
essential concepts and relationships
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Get an overall sense of the data
Distinguish primary/main and secondary/subthemes
Separate essential from non-essential data
Use visual devices – e.g. matrices, diagrams
Processes in qualitative data analysis
6. 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
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Credibility of attributed meaning
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Consistent with data collected
Verified with respondents
Present multiple perspectives (convergent and
divergent views)
Did you go beyond what you expected to find?
Processes in qualitative data analysis
Interpretation
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Dependability
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Can findings be replicated?
Multiple analysts
Confirmability
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Audit trail
– Permits external review of analysis decisions
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Transferability
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Apply lessons learned in one context to another
– Support, refine, limit the generalisability of, or propose
an alternative model or theory