From Data to Evidence
Understanding Women's Participation in Livelihood Skills Development Training
An interactive MEAL learning exercise exploring how quantitative and qualitative analysis can contribute to better programme understanding.
01 — Quantitative analysis
What Happened? — Quantitative Analysis
Source: Quant_Analysis sheet (formula-driven from Raw_Data).
Camp-wise attendance (of 25 invited per camp)
Hover bars for detailInterpretation. Participation declines from Camp A (72%) to Camp D (48%) — a 24-point range across camps of equal size. This tells us where participation was lower, not why.
“Quantitative data shows the scale and distribution of participation, but it does not independently explain why some participants did not attend.”
02 — Qualitative analysis
Why Might It Have Happened? — Exploring Qualitative Data
QR-001“I could not attend because no one was available to care for my young children.”
No caregiver availableChildcare responsibilitiesQR-002“My baby needed me at home during the training hours.”
Infant care needsChildcare responsibilitiesQR-003“The session conflicted with the time I collect my children from school.”
School pickup conflictChildcare responsibilitiesQR-004“I would need a safe childcare option near the training venue.”
Need childcare supportChildcare responsibilitiesSource: Qual_Analysis sheet — 20 fictional responses (QR-001 to QR-020), 5 themes × 4 coded examples. These are not responses from, or findings about, the 100 participants in Raw_Data.
03 — Triangulation
Bringing Evidence Together — Triangulation
Methodological note
The qualitative practice responses are separate from the participant dataset. They cannot be used to establish the reasons for non-attendance.
01 · Quantitative finding
What the data establishes
Overall participation was 60% (60 of 100).
02 · Qualitative practice insight
What the simulated examples illustrate
In simulated practice responses, all five themes appear; each has 4 coded examples.
03 · Interpretation
What can reasonably be inferred — and what needs investigation
Quantitative data establish the scale of participation; simulated qualitative data demonstrate how future inquiry might explore context without making causal claims.
04 · Possible programme action
Proposed, not implemented
Conduct a structured barrier assessment with informed consent and disaggregate responses by camp and relevant inclusion characteristics.
Source: Triangulation sheet, rows 1–4 (verbatim).
04 — Programme learning
From Evidence to Programme Learning
Conduct structured barrier assessments
Conduct a structured barrier assessment with informed consent and disaggregate responses by camp and relevant inclusion characteristics.
Review participation patterns by camp
Review outreach and participation patterns by camp, then co-design context-specific adjustments with women and community stakeholders.
Validate demand before piloting adjustments
Pilot childcare, schedule, transport and accessibility options only after validating demand; track uptake and feedback.
Improve data documentation and future learning
Document data provenance, sampling limitations, referral pathways and a learning agenda for the next training cycle.
Consulting women and community stakeholders runs through these actions — see action 02 on co-design.
Good MEAL Analysis Goes Beyond Numbers.
Quantitative analysis helps us understand what happened. Qualitative analysis helps us explore experiences and context. Triangulation helps us examine evidence together and identify better questions for programme decisions.
Created as an independent MEAL learning and digital data storytelling project.
All data are illustrative or simulated for learning purposes.