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  1. M&E Library
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  3. Ethics in M&E
  4. ENFRES

Ethics in M&E

The principles and standards that guide the ethical conduct of monitoring and evaluation, protecting the rights and dignity of participants, ensuring honest reporting, and managing power responsibly.

When to Use

Ethical considerations apply to every M&E activity involving human participants - which is almost all of them. They become critical when:

  • Working with vulnerable populations: children, survivors of violence, people living with HIV, refugees, or displaced persons
  • Collecting sensitive data: income, sexual behaviour, violence exposure, political views
  • Conducting evaluations where findings could affect funding, employment, or community standing
  • Working in politically sensitive contexts where evaluation findings could be misused

Ethics in M&E is not a compliance checkbox. It is an ongoing commitment to respecting the people whose lives are being studied and whose voices inform the evidence.

How It Works

Core Ethical Principles

The American Evaluation Association (AEA) and the UNEG Ethical Guidelines for Evaluation define five core principles:

  1. Respect for persons - treat participants as autonomous agents with the right to make informed decisions about participation
  2. Beneficence - maximise benefits and minimise harms for participants, communities, and society
  3. Justice - distribute the benefits and burdens of M&E fairly; ensure marginalised groups are not disproportionately studied without benefit
  4. Transparency - be honest about purpose, methods, limitations, and findings
  5. Competence - only conduct M&E for which you have the skills, knowledge, and resources to do responsibly

Informed Consent

Every participant in M&E data collection must be informed about the purpose of the research, how their data will be used, who will see it, what the risks are, and their right to refuse or withdraw. Consent must be voluntary - not coerced by programme incentives or power relationships. For vulnerable populations (children, people with cognitive impairments), additional protocols apply.

Data Privacy and Confidentiality

Personal data collected in M&E must be stored securely, accessed only by those with a legitimate need, and de-identified before use in reports. Data collected for monitoring cannot be repurposed for other uses without renewed consent. Digital data must be encrypted; field devices must be password-protected.

Do No Harm

Evaluation activities must not expose participants to physical, social, economic, or psychological harm. This includes: not collecting sensitive data in communities where disclosure could create safety risks, not publishing identifiable case studies without consent, and mapping referral pathways for participants who disclose distressing experiences during interviews. (see do no harm)

Power and Positionality

Evaluators hold power over the people they study. M&E activities conducted by outsiders can marginalise local knowledge, impose external frameworks, and produce findings that benefit donors more than communities. Ethical practice requires evaluators to be explicit about their positionality and to design processes that give participants genuine voice.

Honest Reporting

Findings must be reported honestly, including negative results. Selective reporting - presenting only findings that reflect well on a programme - is an ethical failure as well as a technical one. Evaluators who face pressure from clients to suppress negative findings should seek professional support through their association's ethics guidelines.

Key Components

  • Informed consent protocol - documented process for obtaining and recording consent
  • Data privacy plan - specifying how data is stored, who accesses it, and when it is deleted
  • Do No Harm assessment - identifying potential harms and mitigation measures
  • Ethical review process - formal review for evaluations involving vulnerable populations (IRB equivalent)
  • Positionality statement - evaluator's acknowledgement of their position relative to participants
  • Referral pathway documentation - for evaluations where participant distress is possible
  • Honest reporting commitment - documented agreement on what will be reported and to whom

Best Practices

Build ethics into the design, not the protocol addendum. Ethical considerations should shape what questions are asked, how data is collected, and how findings are reported - not just consent forms added to a finished instrument.

Close the feedback loop. Share evaluation findings back with participating communities in an accessible format. The people whose lives were studied have a right to see what was concluded.

Maintain independence under pressure. Evaluators working for the same organisation they are evaluating, or paid by the programme being assessed, face structural conflicts of interest. Make independence arrangements explicit in the Terms of Reference.

Train enumerators on ethics, not just methods. Field staff conducting surveys and interviews are the frontline of ethical practice. Training must include what to do when participants disclose harm, how to handle sensitive data, and when to stop an interview.

Common Mistakes

Treating consent as a signature, not a process. Getting a signature on a consent form while rushing through the explanation satisfies legal requirements but not ethical ones. Consent is meaningful only when participants genuinely understand what they are consenting to.

Collecting sensitive data without a referral pathway. Asking questions about violence, trauma, or abuse without a pathway to support services exposes participants to harm. If you ask about GBV, you must have GBV referral options ready.

Publishing identifiable case studies without consent. "Success stories" featuring named beneficiaries, their photographs, and details of their personal circumstances require explicit, informed consent - not just a general data consent form.

Ignoring ethics in secondary data use. Using existing datasets collected for other purposes without ethical review is a common and underappreciated problem, particularly as administrative data becomes more available.

Related Topics

  • Do No Harm - the specific principle requiring that M&E activities avoid causing harm to participants
  • Accountability Mechanisms - systems for being answerable to communities, including for ethical conduct
  • Data Management - the technical systems for managing participant data securely
  • Gender-Responsive M&E - applying ethics with particular attention to gender-based power dynamics
  • Participatory Evaluation - an approach that addresses ethical power imbalances through genuine participation

Further Reading

  • American Evaluation Association (2018). AEA Guiding Principles for Evaluators. www.eval.org. The professional ethics standard for evaluation.
  • UNEG (2008). Ethical Guidelines for Evaluation. United Nations Evaluation Group. UN system standard.
  • Hugman, R. et al. (2011). "Some Considerations on Ethics in Social Work and Social Development." International Social Work, 54(2), 248-259.
  • ACAPS (2019). Ethical Considerations in Humanitarian Settings. Practical guidance for humanitarian M&E contexts.

At a Glance

Ensures that M&E activities respect the rights, dignity, and safety of participants, and that findings are reported honestly and used responsibly.

Best For

  • All M&E activities involving human participants
  • Evaluations working with vulnerable populations (survivors, children, displaced persons)
  • Organisations required to demonstrate ethical practice to donors or institutional review boards
  • Evaluators navigating power imbalances between researchers and participants

Linked Indicators

28 indicators across 5 donor frameworks

AEAUNEGCHS AllianceUSAIDUNICEF

Examples

  • Proportion of evaluations with documented informed consent procedures
  • Percentage of evaluators trained in research ethics within last 2 years
  • Evidence of ethical review for evaluations involving vulnerable populations

Related Topics

Overview
Do No Harm
The foundational M&E principle that programme and evaluation activities must not expose participants, communities, or programme staff to physical, psychological, social, or economic harm, and must actively identify and mitigate harm risks before they occur.
Overview
Accountability Mechanisms
The systems, processes, and structures that enable organisations to answer to stakeholders, including communities, donors, and partners, for their performance, decisions, and use of resources.
Overview
Data Quality Assurance
A systematic process for verifying that collected data meets five quality dimensions, Validity, Integrity, Precision, Reliability, and Timeliness, ensuring data is fit for decision-making.
In-Depth Guide
Participatory Evaluation
An evaluation approach that actively involves stakeholders and beneficiaries throughout all stages, from design through use of findings, ensuring local ownership and relevance.
Overview
Gender-Responsive M&E
An approach to monitoring and evaluation that systematically examines how programmes affect women, men, girls, and boys differently, and ensures that M&E processes themselves do not reinforce gender inequalities.
Overview
Data Management
The systematic processes for collecting, storing, securing, and maintaining data quality throughout the data lifecycle to ensure information is accurate, accessible, and usable for decision-making.

Related Guides

How to Protect Data Privacy When Using AI for M&E
Beneficiary data belongs to beneficiaries, not AI servers. The SAFE Framework helps you use AI tools without risking a data protection breach, donor compliance violation, or loss of community trust.
How to Build AI Governance for Your M&E Function
Every major donor now expects some form of AI governance. This 6-point framework, synthesized from UN, UK, World Bank, and EU requirements, gives you a defensible structure before your first AI pilot.
How to Run a Data Protection Impact Assessment for AI in M&E
DPIAs are becoming standard for AI use in evaluation and monitoring. This 4-step process helps you assess risks before uploading any data to an AI tool, not after something goes wrong.
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