M&E Data Collection Specialist
Design surveys and manage M&E data quality for development programs
What You Can Do
You can design culturally appropriate survey instruments tailored to international development contexts, analyze mixed-methods data by integrating quantitative findings with qualitative insights, and implement robust data quality management systems for your field operations. Claude helps you create survey templates with adaptive logic, develop validation protocols, and produce data quality reports that highlight issues before analysis begins.
Features
Create culturally appropriate questionnaires with branching logic, response validation rules, and adaptive questions based on previous answers
Integrate quantitative findings (statistics, distributions) with qualitative insights (themes, quotes) into cohesive evaluation narratives
Build validation protocols, implement data cleaning checklists, and generate quality reports identifying missing values, outliers, and inconsistencies
Develop statistically sound sampling approaches (stratified, cluster, purposive) aligned with your program scope and budget constraints
Create stakeholder-ready dashboards that visualize key indicators, trends, and comparisons for different audience levels (board vs. field teams)
Design QA frameworks and training materials for enumerator teams, including spot-checks, back-checks, and data entry verification procedures
Generate comprehensive data dictionaries mapping variables to definitions, response codes, skip patterns, and quality flags for transparency and reproducibility
Example Output
Survey Instrument Sample:
Q1: What is your primary livelihood? (Single select)
- [ ] Agriculture
- [ ] Livestock
- [ ] Small business
- [ ] Wage labor
- [ ] Other (specify)
IF Q1 = Agriculture SHOW Q2
Q2: How many hectares do you cultivate? ____
IF Q2 > 5 SHOW Q3, ELSE SKIP Q3
Data Quality Report Excerpt:
- Missing values: 3.2% (below 5% threshold) ✓
- Outliers identified: 8 responses (reviewed, 6 retained) ✓
- Logical inconsistencies: 12 cases flagged (enumerator re-contact completed) ✓
- Overall quality score: 94% — Data approved for analysis
Mixed-Methods Finding:
- Quantitative: 67% of farmers adopted improved seeds (n=156)
- Qualitative theme: "Fear of failure with new varieties" cited in 8/22 interviews
- Integration: Adoption barriers are psychological, not technical—training must address confidence, not just technique
What's Included
- Survey Template Library: Pre-built instrument templates for common development topics (education, health, agriculture, WASH) ready for context-specific adaptation
- Data Quality Checklist: Step-by-step validation protocols for field teams, including logical consistency checks, range validation, and data entry verification procedures
- Mixed-Methods Integration Guide: Framework for combining quantitative statistics with qualitative themes, linking numbers to stories for compelling evaluation narratives
- QA Training Materials: Enumerator training decks covering survey administration, quality expectations, spot-check procedures, and how to handle common field challenges
- Data Dictionary Template: Structured format for documenting all variables, response codes, skip logic, data type, and quality flags for complete transparency
- Dashboard Template Repository: Pre-designed visualization layouts for different audiences (donors want outcomes; field teams want operational metrics; boards want trends)
Who It's For
- M&E Officers and Coordinators
- Development Program Managers
- Research Teams at NGOs and International Organizations
- Impact Evaluation Specialists
- Monitoring Data Analysts
Best For
- Designing field survey instruments with adaptive logic
- Conducting mixed-methods data analysis and synthesis
- Building data quality assurance frameworks
- Creating evaluation dashboards for stakeholder reporting
- Training data collection teams on quality standards
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