Data collection, quality and analysis

1Data collection methods

• Quantitative: household surveys, counts, distribution registers, health data. They tell us “how many”.
• Qualitative: focus group discussions (6 to 10 people from a homogeneous group, by sex and age), key informant interviews, direct observation, case studies. They tell us “why” and “how”.
• Mixed methods: combining the two, together with triangulation (comparing several sources), strengthens the reliability of conclusions.

Common digital tools: KoboToolbox and ODK (forms on mobile phones that work offline), then Excel, Power BI or R for analysis.

2Sampling

We rarely survey the entire population: we draw a representative sample.

Sample size for a proportion: n = z² × p × (1 − p) / e². With a 95% confidence level (z = 1.96), an unknown proportion (p = 0.5) and a 5% margin of error (e = 0.05): n = 3.8416 × 0.25 / 0.0025 ≈ 384 households.

• If the population is small, apply the finite population correction: n' = n / (1 + (n − 1) / N). For N = 1,000 households, n' ≈ 278.
• In cluster sampling (randomly selected villages), multiply by the design effect, often 1.5 to 2.
• Add 5 to 10% for refusals and absences.
• Methods: simple random (complete list), systematic (one household in every k), stratified (by area or group), cluster. For qualitative work, participants are selected purposively.

3Data quality

Five criteria: validity (we really measure what we intend to), reliability (the same result if repeated), precision, timeliness and integrity (no manipulation).

• Train and test enumerators, pre-test the questionnaire.
• Constraints and checks in the digital form (impossible values, required fields).
• Supervision and revisits to a sample of households (back-checks).
• Data quality assessments (DQAs) for key indicators.

4Analysing and using data

Useful analysis answers a decision-making question. Compare with the baseline and the target, disaggregate (women, men, girls, boys, people with disabilities, areas), and look for gaps and their causes using qualitative data.

Post-distribution monitoring (PDM) checks, a few weeks after a distribution, whether the assistance arrived, how it was used and whether it met needs. Present the results on one page, with simple charts and 3 recommendations, at a meeting where the team decides on corrective action.