Research Design & Methodology for a Multiple Site Value Chain Assessment
International development | Mixed-method research | Survey design | Sampling | Analytical framework
Wells supported the research design and methodological development of a multi-site fisheries value-chain assessment for an international development programme. The work translated broad sector and investment questions into a structured mixed-method framework covering sampling, survey design, data collection, stakeholder groups, analytical requirements and field implementation.
The aim was to ensure that evidence collected across landing sites, markets, enterprises and stakeholder groups could be compared consistently and translated into decision useful findings.
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Research Challenge
Broad programme objectives needed to be translated into a consistent methodology for collecting comparable evidence across multiple sites, stakeholder groups and value-chain activities.
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Methodological Support
Research framework development, survey methodology, sampling, variable definition, mixed-method data requirements and analytical planning. -
Outcome
A field ready methodological framework linking research objectives to data collection, analysis and decision making.
The Challenge
The programme needed evidence capable of informing future investment decisions across a complex and geographically dispersed fisheries system. Broad research objectives had already been established, but those objectives still needed to be translated into an implementable methodology before fieldwork could begin.
Key methodological challenges included:
- Translating broad development objectives into measurable variables and indicators
- Designing data collection across landing sites, markets, enterprises and different stakeholder groups
- Establishing sampling approaches suitable for multiple locations and respondent populations
- Integrating quantitative survey data with qualitative interviews and stakeholder consultation
- Capturing economic, social, infrastructure and environmental dimensions within one research framework
- Incorporating gender and youth disaggregation
- Accounting for seasonal variation in fisheries activity
- Establishing consistent data-quality procedures for field collection
- Ensuring that the evidence collected could ultimately support defensible investment and programme decisions
The Methodological Approach
1. Translate objectives into measurable research components
The programme objectives were mapped to specific variables, indicators, respondent groups and data requirements so that each component of field collection had a clear analytical purpose.
2. Structure the research across multiple levels of enquiry
The methodology integrated three connected levels: landing and market infrastructure, enterprise-level conditions and wider value-chain performance.
3. Combine quantitative and qualitative evidence
Survey data were designed to work alongside interviews, stakeholder consultation and secondary evidence rather than being interpreted in isolation.
4. Build consistency into field collection
Sampling, survey structure, variable definitions and data-quality procedures were developed to support comparable data collection across sites and field teams.
5. Design backwards from the decisions the evidence needed to support
The analytical framework was considered during study design so that the data collected could ultimately contribute to market analysis, value-chain mapping, bottleneck identification and investment prioritisation.
The Outcome/Impact
The methodological work transformed a broad and complex research brief into a structured, field-ready framework linking research objectives, sampling, survey design, data collection and analysis.
By standardising how information was collected across multiple sites and stakeholder groups, the framework helped improve consistency, reduce the risk of incomplete or unusable data, and ensure that the evidence gathered remained directly connected to the programme’s decision-making needs.
The work also created a stronger basis for identifying value-chain bottlenecks, comparing conditions across locations, prioritising potential interventions and establishing evidence that could support future programme monitoring and baseline comparison.
Greater consistency
A more standardised approach to collecting comparable data across sites, teams and stakeholder groups.
More decision-relevant evidence
Research questions, variables and analytical requirements were connected from the outset.
Stronger basis for future planning
The resulting framework supported investment prioritisation and provided a foundation for future comparison and programme monitoring.
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Why This Matters
Explore Research Design & Methodology ConsultancyGood data does not begin with data collection. It begins with a clear research structure.
In complex projects, decisions about what to measure, who to include, how variables are defined, and how different sources of evidence fit together determine whether the final dataset can actually answer the original research questions.
This project showed the value of building the methodology first: linking research objectives to sampling, survey design, field data collection, analysis and decision-making before the work progressed further.
By creating that structure early, the project was better positioned to generate consistent, comparable and decision-relevant evidence across multiple sites and stakeholder groups.