How Can Assumptions Negatively Impact the Effectiveness of Your Monitoring and Evaluation (M&E) System?

Assumptions play an important role in Monitoring and Evaluation (M&E) systems, particularly when drafting the Theory of Change (ToC) and the Logical Frameworks (LogFrames) for a new development project. Incorrect or untested assumptions can lead to inaccurate data, flawed decision-making, and ineffective project outcomes. Here are cases in which assumptions can negatively impact an M&E system:

Misalignment Between Expected and Actual Results

When assuming that an intervention will automatically lead to the desired outcomes (without considering external factors, context or behavioural responses). In that case, data collection efforts may focus on the wrong indicators. The system may track irrelevant indicators, leading to incorrect conclusions about project effectiveness.

Poor Indicator Selection and Measurement Issues

If an M&E system assumes that reliable data sources exist or that stakeholders will provide accurate data, it may fail to assess feasibility before setting up indicators. Indicators may be difficult to measure, inconsistent, or in some cases invalid.

Ignoring External Risks and Contextual Factors

Many M&E systems assume a stable environment, ignoring political, economic, or social risks that can influence results. The system may fail to detect unexpected changes, leading to inaccurate reporting and poor adaptation.

Data Collection and Interpretation Bias

If assumptions about how stakeholders interpret survey questions, feedback mechanisms, or qualitative indicators are incorrect, data may be biased or misrepresented. The M&E system may produce skewed insights, affecting program adjustments and resource allocation.

Frameworks That Cannot Adapt to Change

If an M&E system assumes that the project context will remain static, it may not include mechanisms to review and revise indicators based on real-time learning. The system becomes inflexible, making it difficult to adjust strategies based on emerging evidence.

Failure to Capture Long-Term or Indirect Outcomes

If an M&E system assumes that project impacts will be immediate and direct, it may fail to track long-term or indirect effects. Evaluations may underreport success or overlook unintended negative consequences.

Unrealistic Expectations About Stakeholder Engagement

Assumptions that stakeholders will consistently participate in M&E activities, provide honest feedback, or use findings for decision-making can be misleading. Low engagement affects data quality where M&E findings fail to influence program improvements.

How to Mitigate These Risks?

• Clearly document and test assumptions in the ToC or LogFrame.

• Regularly review assumptions using risk assessments and adaptive learning approaches.

• Use diverse data sources to validate findings and reduce bias.

• Ensure flexibility in M&E frameworks to adjust indicators based on emerging insights.

• Engage stakeholders continuously to refine assumptions and improve data accuracy.