• Problem formulation: framing research questions; constructing testable hypotheses/propositions; literature synthesis and gap identification; developing conceptual/theoretical frameworks
• Understanding of skill ecosystem: Prior work or academic experience of skill ecosystem
• Methodological design: selecting research paradigm (quantitative / qualitative / mixed); choosing design (survey, experiment, case study, ethnography, longitudinal); operationalising constructs into measurable variables
• Sampling design: probability vs. non-probability strategies; sample-size and power estimation; weighting and representativeness; frame construction
• Instrument development: questionnaire and item writing; interview/FGD protocol design; scale development, piloting and validation
• Research governance: ethics and informed consent; securing IRB/ethical clearance and administrative approvals; data-protection and privacy compliance
B — Data Collection & Fieldwork
• Primary collection:
survey administration (CAPI/CATI/paper); interviewing (rapport, probing, active listening); structured observation and field recording
• Field operations: enumerator recruitment, training and supervision; fieldwork logistics and scheduling; real-time monitoring
• Secondary data acquisition: sourcing administrative and government datasets; extraction, compilation and linkage; assessing provenance and fitness-for-use
• Data quality assurance: back-checks and spot validation; consistency and range checks; audit trails
C — Data Management & Analysis
• Data preparation: cleaning, de-duplication and outlier handling; missing-data treatment; coding, structuring and codebook/metadata documentation