SayPro List 100 common data discrepancies found in M&E reports and how to correct them.”

Certainly! Here’s a detailed list of 100 common data discrepancies found in Monitoring & Evaluation (M&E) reports—along with corresponding corrective actions for each. The list is categorized for easier understanding.

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I. Data Entry Errors

Discrepancy	Corrective Action

1. Typographical errors | Cross-check entries with source documents; correct and train staff.
2. Duplicate records | Deduplicate using unique IDs or filtering software tools.
3. Misaligned data columns | Review templates and align headers consistently.
4. Data entered in wrong fields | Train users and implement validation rules.
5. Copy-paste errors | Spot-check and compare with originals.
6. Omitted entries | Revisit source records and update missing data.
7. Incorrect date formats | Standardize date inputs (e.g., DD/MM/YYYY).
8. Decimal errors (e.g., 2.00 instead of 200) | Recalculate and verify from original forms.
9. Use of mixed number formats (e.g., comma vs dot for decimals) | Standardize format based on system settings.
10. Formula errors in spreadsheets | Audit and fix incorrect formulas using cell tracing.

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II. Indicator Misinterpretation

Discrepancy	Corrective Action

11. Misunderstood indicator definitions | Provide clear guidance and job aids.
12. Confusion between numerator and denominator | Offer refresher training and validation checks.
13. Mixing up units (e.g., percentages vs absolute numbers) | Clarify expected units in reporting templates.
14. Inconsistent age disaggregation | Enforce consistent disaggregation criteria.
15. Gender data reversed or missing | Validate against registration records and retrain.
16. Over-reporting due to double-counting | Use unique identifiers or registration systems.
17. Under-reporting due to unrecognized sources | Cross-check all data sources for completeness.
18. Aggregating incompatible indicators | Define aggregation rules; separate incompatible data.
19. Overlooking null values as 0 | Differentiate between “zero” and “no data.”
20. Inclusion of ineligible participants | Review criteria and filter data based on eligibility.

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III. Source Data Inconsistencies

Discrepancy	Corrective Action

21. Paper records don’t match digital versions | Reconcile with field logs or re-enter correctly.
22. Register totals don’t match summary sheets | Recalculate manually and verify.
23. Missing supporting documents | Request re-submission or recreate where possible.
24. Different figures across departments | Standardize and sync departmental databases.
25. Weekly/monthly totals don’t add up | Cross-check summations and calculations.
26. Use of outdated templates | Distribute current templates and phase out old ones.
27. Mismatched village/district codes | Update master lists and conduct data cleaning.
28. Inconsistent spellings of locations | Use dropdowns or coded entries.
29. Old versions of tools in use | Replace and archive old tools; provide training.
30. Data submitted outside reporting period | Flag and exclude non-compliant data.

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IV. System and Software-Related Issues

Discrepancy	Corrective Action

31. Data loss during sync | Use cloud backups and test sync processes.
32. Software updates causing field changes | Confirm compatibility before updates.
33. Auto-calculation errors | Manually verify system-generated figures.
34. Incomplete data uploads | Conduct routine system checks and monitor completeness.
35. Misconfigured dropdowns or selections | Reconfigure and test input options.
36. Export errors (e.g., wrong file format) | Use correct export settings and reformat.
37. Duplicated rows in database imports | Use import validation tools.
38. Data overwritten by concurrent users | Apply locking features or time stamps.
39. Inadequate system permissions | Implement role-based access controls.
40. Data entry not saved | Train on save/submit protocols; improve UI alerts.

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V. Aggregation and Calculation Errors

Discrepancy	Corrective Action

41. Incorrect totals in summary tables | Review calculations and verify source totals.
42. Averaging numbers instead of summing | Revise formulas; use correct statistical methods.
43. Misuse of weighted averages | Use approved statistical methods.
44. Rounded figures misrepresent data | Use standard rounding rules; retain decimal precision.
45. Percentages that exceed 100% | Audit calculation logic; correct denominators.
46. Annual totals not matching monthly breakdowns | Recheck summation; reconcile inconsistencies.
47. Negative values in non-negative indicators | Investigate anomalies and correct misentries.
48. Double-counting in aggregations | Use unique participant IDs or remove overlaps.
49. Improper merging of datasets | Use consistent keys and pre-merge cleaning.
50. Combining cumulative and non-cumulative indicators | Separate and report accurately.

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VI. Time-Series and Trend Errors

Discrepancy	Corrective Action

51. Inconsistent reporting periods | Align timelines and ensure proper date tagging.
52. Trends that contradict previous reports | Investigate changes in method or context.
53. Missing months/quarters in time series | Fill gaps with field verification or annotate accordingly.
54. Seasonal fluctuations misinterpreted as errors | Adjust analysis based on context.
55. Rolling averages misused | Clarify definitions and recalibrate methods.
56. Misaligned baseline and endline comparisons | Recalculate using comparable periods.
57. Incorrect year-to-date summaries | Re-sum from source data.
58. Comparing data with different denominators | Standardize or use ratios.
59. Gaps in long-term trend lines | Document and explain gaps.
60. Comparing incompatible data collection tools | Note methodology differences or exclude.

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VII. Human Error and Process Gaps

Discrepancy	Corrective Action

61. Incomplete data collection | Retrain and supervise field teams.
62. Delays in reporting cause outdated data | Establish stricter timelines and escalation.
63. Misreporting due to pressure to show success | Promote data use ethics and neutrality.
64. Miscommunication between field and M&E teams | Enhance feedback loops.
65. Errors due to poor handwriting | Digitize and standardize data entry.
66. Uncalibrated measurement tools | Conduct regular checks and calibrations.
67. Misinterpretation of instructions | Simplify and clarify data collection guidelines.
68. Use of wrong versions of forms | Decommission old tools systematically.
69. Field staff using unofficial tools | Standardize and distribute official materials.
70. Language barriers in data collection | Translate tools and train in local dialects.

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VIII. Reporting and Formatting Issues

Discrepancy	Corrective Action

71. Mislabelled charts or tables | Review titles and legend accuracy.
72. Inconsistent formats across reports | Use style guides and templates.
73. Wrong decimal places | Define acceptable rounding standards.
74. Tables missing data labels | Include labels and annotations.
75. Use of incompatible file types | Provide standard reporting formats.
76. Text summaries not matching tables | Cross-verify narrative and numerical data.
77. Poor chart scaling hides trends | Adjust axes and scales appropriately.
78. Ambiguous footnotes or metadata | Improve documentation clarity.
79. No version control on reports | Implement naming/version conventions.
80. Incomplete or unclear legends | Ensure clarity and consistency.

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IX. Contextual and Logical Errors

Discrepancy	Corrective Action

81. Reporting more outputs than inputs justify | Cross-check logical frameworks.
82. Indicators contradict each other | Validate through logic checks.
83. Data inconsistent with known local conditions | Investigate local context and adjust assumptions.
84. Unrealistic achievements within timeframes | Validate field activity logs.
85. Targets exceeding 100% inexplicably | Confirm target-setting logic.
86. Unexpected jumps or drops in results | Investigate and document reasons.
87. Incompatible results across regions | Standardize data collection approaches.
88. Fictitious GPS coordinates | Use live maps to verify data locations.
89. Household sizes far above/below average | Recheck with field sources.
90. Demographic figures inconsistent with census data | Revalidate sampling and coverage.

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X. Ethical and Compliance Issues

Discrepancy	Corrective Action

91. Missing consent records | Recollect with consent or exclude.
92. Data reported without disaggregation when required | Recollect or estimate with caution.
93. Sensitive data not anonymized | Apply masking or pseudonymization.
94. Non-compliance with donor formats | Align with donor templates and guidelines.
95. Reported data outside project scope | Remove or clearly label as anecdotal.
96. Ineligible entities included (e.g., non-beneficiaries) | Review eligibility criteria.
97. Data tampering or falsification | Investigate and escalate per policy.
98. Inconsistent beneficiary IDs | Introduce ID verification systems.
99. Inflation of numbers for recognition | Reinforce data integrity culture.
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