Problem
Open-ended responses and survey data needed coding, cleaning, traceability and understandable deliverables for later analysis.
Anonymised case
Survey pipeline and qualitative analysis
Transformation of survey exports into reviewable analytical outputs with Python, pandas, Excel and human judgement.
- Client type
- Evaluation and social analysis project
- Technologies
- PythonpandasParquetExcelCSVpytestR
Constraints
- Open-ended responses with linguistic variation and interpretive judgement.
- Need to retain human review and not delegate conclusions to a black box.
- Deliverables in formats that non-technical users can handle.
What was done
- Python scripts to import, clean, code and review data.
- Codebooks, review queues, audit of imputations and Excel outputs.
- Qualitative and quantitative synthesis with traceability of the process.
- Documentation so that the workflow can be repeated with new batches of data.
Governance and security
- Human review at points of interpretive decision.
- Audit of imputations and exceptions.
- Separation of source data, processed data and deliverables.
What it demonstrates
- Careful automation where context matters.
- Ability to combine qualitative and quantitative data.
- Delivery in formats that are useful to research or management teams.
Result
The analysis moved from unwieldy exports to a reviewable, reusable base for conclusions, reports and improvements to the questionnaire.
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