You are a senior MEAL specialist with expertise in data quality assurance. Your task is to create a comprehensive Data Quality Audit (DQA) Protocol for "program name" (from the material, if given).
Program Context:
- Program name: "program name" (from the material, if given)
- Number of implementation sites: "number" (from the material, if given)
- Key indicators to audit: "indicators" (from the material, if given)
- Data collection tools: "tools" (from the material, if given)
- Reporting frequency: "frequency" (from the material, if given)
- Donor: "donor" (from the material, if given)
Deliverables:
1. Audit Objectives and Scope
- Purpose of the DQA (verification, improvement, compliance, or all three)
- Specific data quality dimensions to assess: Validity, Reliability, Completeness, Timeliness, Precision, and Integrity (define each in the program context)
- Time period under review
- Data sources and levels to audit (facility, district, national)
2. Sampling Strategy
- Sampling frame: list of all sites and reporting units
- Sample size calculation: recommend auditing at least 10-15% of sites, with justification
- Selection method: random sampling with stratification by "performance level, geography, or facility type" (from the material, if given)
- Indicator sampling: which indicators to audit in full versus spot-check
- Document sampling: how many records to verify per site (recommend minimum 20 records or 10% of total, whichever is larger)
3. Verification Procedures For each data quality dimension, provide specific verification steps:
| Dimension | Verification Method | Data Source Cross-Reference | Pass Criteria |
|---|
Include:
- Source document verification: Compare reported figures against original source documents (registers, forms)
- Recounting: Independently recount a sample of records and compare with reported totals
- Cross-system checks: Compare figures across parallel systems (e.g., facility register vs. digital form vs. monthly report)
- Timeliness check: Percentage of reports submitted on time
- Completeness check: Percentage of required fields completed in source documents
- Logic checks: Identify impossible or implausible values (e.g., children treated exceeding children screened)
4. Scoring Rubric Create a scoring matrix for each dimension on a 1-4 scale:
| Score | Label | Description |
|---|---|---|
| 4 | Highly Satisfactory | Data meet all quality standards with minor issues only |
| 3 | Satisfactory | Data are generally reliable with some correctable gaps |
| 2 | Needs Improvement | Significant quality issues that affect data reliability |
| 1 | Unsatisfactory | Data cannot be relied upon for decision-making |
Define what constitutes each score for each dimension. Provide an overall composite scoring method with weighting.
5. Audit Tools and Templates List and briefly describe each tool needed:
- Site-level verification checklist
- Record recounting worksheet
- Data cross-reference matrix
- Timeliness tracking log
- Findings summary template
- Corrective action plan template
6. Corrective Action Framework For each score level (1-4), specify:
- Required corrective actions
- Responsible party
- Timeline for remediation
- Follow-up verification date
- Escalation protocol if issues persist
7. Audit Schedule and Team
- Recommended audit frequency (quarterly for high-risk indicators, semi-annually for routine)
- Team composition and roles
- Estimated level of effort per site (person-days)
- Budget considerations
Align with USAID DQA guidelines, Global Fund LFA verification standards, and MEASURE Evaluation data quality frameworks.
Where the material is silent, write "Not in the material" rather than guessing.
Source material: Attached, or pasted below. Base everything above on it and do not invent details that are not in it.