# monival.org

> A citation-grade reference on monitoring and evaluation practice — evaluation designs, indicators, data quality, MEAL systems and standards — published and maintained by Sibasi Ltd.

Published and maintained by Sibasi Ltd (Nairobi, Kenya).
Every reference page opens with an answer-first summary and key points,
and closes with a numbered bibliography of verifiable sources. Content may
be cited with attribution to monival.org.

The related product — the Monival M&E platform — lives at https://monival.com.

## Reference sections

- [Evaluation designs and causal inference](https://monival.org/methods): Randomised trials, difference-in-differences, regression discontinuity, matching, interrupted time series, synthetic control, and the theory-based methods that work where none of those fit.
- [Evaluation approaches](https://monival.org/approaches): Utilization-focused, developmental, participatory, empowerment, feminist and gender-responsive evaluation, Most Significant Change and Outcome Mapping.
- [Indicator design and measurement](https://monival.org/indicators): Indicator types and levels, quality criteria, reference sheets, proxy and composite measures, and baselines, targets and disaggregation.
- [Data quality, sampling and collection](https://monival.org/data): Data quality dimensions and assessment, sampling, questionnaire design, qualitative methods and combining the two traditions.
- [MEAL, ethics and safeguarding](https://monival.org/meal): Accountability to affected populations, feedback and complaints mechanisms, learning and adaptive management, research ethics and consent, and data protection.
- [Monitoring systems and government M&E](https://monival.org/systems): M&E information systems, DHIS2, results-based management, national M&E systems, and performance contracting in Kenya.
- [Project and strategy M&E](https://monival.org/management): The project-management interface, earned value management, risk registers, balanced scorecard, OKRs and KPI cascades, and mid-term reviews.
- [Reporting, use and communication](https://monival.org/reporting): Evaluation report structure, dashboards and data visualisation, and management response and follow-up.
- [Norms, standards and quality](https://monival.org/standards): UNEG Norms and Standards, the JCSEE Program Evaluation Standards, the African Evaluation Principles, and how evaluation quality is assessed.

## Reference pages

- [Randomised controlled trials](https://monival.org/methods/randomised-controlled-trials): How RCTs identify causal impact: randomisation logic, unit and level choices, power, threats to validity, ethics, and when not to randomise.
- [Difference-in-differences](https://monival.org/methods/difference-in-differences): What difference-in-differences estimates, the parallel trends assumption it rests on, how to test it, and the pitfalls that break it.
- [Regression discontinuity designs](https://monival.org/methods/regression-discontinuity): When a cutoff assigns a programme, RDD compares units just above and below it. Sharp vs fuzzy designs, bandwidth choice, and validity checks.
- [Matching and propensity score methods](https://monival.org/methods/matching-and-propensity-scores): Constructing comparison groups from observables: propensity scores, matching estimators, balance diagnostics, and the unobservables caveat.
- [Interrupted time series analysis](https://monival.org/methods/interrupted-time-series): Evaluating interventions with a long outcome series and a clear start date: segmented regression, level vs slope change, seasonality, autocorrelation.
- [Synthetic control methods](https://monival.org/methods/synthetic-control): Building a weighted synthetic comparison for one treated region or policy: donor pools, pre-period fit, placebo inference, and feasibility limits.
- [Contribution analysis](https://monival.org/methods/contribution-analysis): Mayne's six-step approach to credible causal claims without a counterfactual: programme theory, evidence, rival explanations, contribution story.
- [Process tracing for evaluation](https://monival.org/methods/process-tracing): Within-case causal inference using evidence tests — straw-in-the-wind, hoop, smoking gun, doubly decisive — applied to programme evaluation.
- [Realist evaluation](https://monival.org/methods/realist-evaluation): What works, for whom, in what circumstances: context–mechanism–outcome configurations, realist programme theory, and RAMESES quality standards.
- [Qualitative comparative analysis (QCA)](https://monival.org/methods/qualitative-comparative-analysis): Cross-case causal analysis with sets: necessary and sufficient conditions, truth tables, crisp vs fuzzy sets, and when medium-N beats regression.
- [Utilization-Focused Evaluation](https://monival.org/approaches/utilization-focused-evaluation): Patton's UFE: design every evaluation around primary intended users and intended uses. Steps, the U-FE checklist, and when UFE goes wrong.
- [Developmental Evaluation](https://monival.org/approaches/developmental-evaluation): Evaluation for innovation under complexity: the evaluator embedded in the team, real-time feedback, and how DE differs from formative evaluation.
- [Participatory and Empowerment Evaluation](https://monival.org/approaches/participatory-and-empowerment-evaluation): Who controls the evaluation? Practical vs transformative participation, Fetterman's empowerment evaluation, and the rigour debate — both sides cited.
- [Feminist and Gender-Responsive Evaluation](https://monival.org/approaches/feminist-and-gender-responsive-evaluation): Evaluation that examines power, not just sex-disaggregated counts: feminist principles, UN Women's gender-responsive practice, and African framings.
- [Most Significant Change (MSC)](https://monival.org/approaches/most-significant-change): The MSC technique: collecting and systematically selecting stories of change, Davies and Dart's ten steps, and what MSC can and cannot claim.
- [Outcome Mapping](https://monival.org/approaches/outcome-mapping): IDRC's Outcome Mapping: boundary partners, progress markers and behaviour-change outcomes for programmes that influence rather than control.
- [Indicator types and levels](https://monival.org/indicators/types-and-levels): Input, output, outcome and impact indicators; quantitative vs qualitative; direct vs proxy; cumulative vs milestone — with precise definitions.
- [Indicator quality criteria: SMART, CREAM, SPICED](https://monival.org/indicators/quality-criteria): The competing quality tests for indicators — SMART, CREAM, SPICED — what each catches, what each misses, and how to run a structured indicator review.
- [Performance indicator reference sheets (PIRS)](https://monival.org/indicators/reference-sheets): The PIRS is the contract behind every indicator: definition, unit, disaggregation, data source, method, frequency, known limitations. Template walkthrough.
- [Proxy indicators and composite indices](https://monival.org/indicators/proxy-and-composite): Measuring the unmeasurable: defensible proxy indicators, and composite indices — normalisation, weighting, aggregation — per the OECD/JRC handbook.
- [Baselines, targets and disaggregation](https://monival.org/indicators/baselines-targets-disaggregation): Setting baselines that exist, targets that mean something, and disaggregation that reveals who is left out — with target-setting methods compared.
- [Data quality dimensions in M&E](https://monival.org/data/quality-dimensions): The working dimensions of M&E data quality — validity, reliability, integrity, precision, timeliness — and how each one fails in practice.
- [Data quality assessment (DQA)](https://monival.org/data/quality-assessment): What a data quality assessment covers, the standard dimensions it measures, and how to run verification and system appraisal that lead to action.
- [Sampling for M&E: methods and sample size](https://monival.org/data/sampling): Probability and purposive sampling for surveys and evaluations: cluster designs, design effects, sample-size logic, and honest small-sample practice.
- [Survey and questionnaire design](https://monival.org/data/questionnaire-design): Writing questions that measure what you mean: wording, translation, ordering, response scales and pretesting — with LSMS-grade sources.
- [Qualitative data collection: interviews, focus groups, observation](https://monival.org/data/qualitative-methods): Running KIIs, FGDs and structured observation that produce analysable evidence: sampling, guides, facilitation, recording, coding and saturation.
- [Mixed methods and secondary data in evaluation](https://monival.org/data/mixed-methods): Designing sequential and concurrent mixed-methods evaluations, triangulation done honestly, and appraising secondary and administrative data.
- [Accountability to Affected Populations (AAP)](https://monival.org/meal/accountability-to-affected-populations): AAP as the responsible use of power: the IASC commitments, CHS 2024, participation in M&E, safeguarding, and information as a form of aid.
- [Feedback and complaints mechanisms](https://monival.org/meal/feedback-and-complaints-mechanisms): Designing feedback and complaints channels that close the loop: channel choice, sensitive-complaint handling, response standards and using the data.
- [Learning agendas, adaptive management and after-action reviews](https://monival.org/meal/learning-and-adaptive-management): Making learning operational: learning agendas, USAID's CLA framing, evidence-informed adaptation, and the after-action review as a disciplined ritual.
- [Research ethics, do no harm and informed consent in M&E](https://monival.org/meal/ethics-and-consent): The ethical floor for M&E data collection: Belmont principles, UNEG ethical guidelines, CIOMS, consent that is real, and when ethical review applies.
- [Data protection for M&E: Kenya's DPA 2019 and the GDPR](https://monival.org/meal/data-protection): What Kenya's Data Protection Act 2019 and the GDPR require of M&E data: lawful basis, minimisation, retention, transfers and breach duties — practically.
- [M&E information systems: data flow, interoperability and architecture](https://monival.org/systems/me-information-systems): Designing the data flow behind an M&E system: sources to decisions, routine vs survey data, interoperability standards, and build-vs-adopt choices.
- [DHIS2 in monitoring and evaluation](https://monival.org/systems/dhis2): What DHIS2 is, where it fits in national health information systems and programme M&E, its data model in brief, and how to work with it, not around it.
- [Results-based management (RBM)](https://monival.org/systems/results-based-management): RBM as a management discipline: the results chain, the managing-for-outcomes lifecycle, UN and World Bank practice, and why RBM reforms fail.
- [National M&E systems](https://monival.org/systems/national-me-systems): How countries institutionalise M&E: whole-of-government systems, Kenya's NIMES and the 2022 National M&E Policy, diagnostic lenses, and African lessons.
- [Performance contracting in Kenya's public service](https://monival.org/systems/performance-contracting-kenya): Kenya's performance contracting regime: the annual PC cycle, cascading targets, evaluation and ranking, the legal frame, and what the evidence supports.
- [The project management–M&E interface](https://monival.org/management/project-management-and-me): Where PM and M&E meet: WBS and milestones as monitoring scaffolding, PMBOK measurement thinking, schedule health, and who owns which data.
- [Earned value management](https://monival.org/management/earned-value-management): EVM in plain terms: planned value, earned value, actual cost; CPI and SPI; forecasting completion — and when EVM is overkill for development projects.
- [Risk registers and risk monitoring](https://monival.org/management/risk-registers): Running a risk register that changes decisions: ISO 31000 process, likelihood-impact scoring, risk appetite, KRIs, and the register-M&E connection.
- [The Balanced Scorecard and strategy maps](https://monival.org/management/balanced-scorecard): Kaplan and Norton's Balanced Scorecard: four perspectives, strategy maps, adaptation for public and nonprofit organisations, and honest critiques.
- [OKRs and KPI cascades in institutions](https://monival.org/management/okrs-and-kpi-cascades): Objectives and key results, KPI cascades and institutional performance reporting: mechanics, public-sector fit, and the gaming risks Goodhart warned about.
- [Mid-term reviews](https://monival.org/management/mid-term-reviews): The MTR as a course-correction instrument: scope, timing, questions, MTR vs evaluation, and turning findings into a re-planned second half.
- [Writing and judging evaluation reports](https://monival.org/reporting/evaluation-reports): The anatomy of a credible evaluation report: structure, evidence-to-finding-to-recommendation chains, executive summaries, and quality checklists.
- [Dashboards and data visualisation for M&E](https://monival.org/reporting/dashboards-and-data-visualisation): Designing M&E dashboards and charts people can act on: audience-first design, chart choice, honest representation, and dashboard anti-patterns.
- [Evaluation use and the management response](https://monival.org/reporting/use-and-management-response): From findings to consequences: types of evaluation use, the management response and action-tracking discipline, and what the use literature shows.
- [The UNEG Norms and Standards for Evaluation](https://monival.org/standards/uneg-norms-and-standards): What the UNEG Norms and Standards require: independence, impartiality, credibility, utility — and how non-UN organisations can use them.
- [The Program Evaluation Standards and the AEA Guiding Principles](https://monival.org/standards/program-evaluation-standards): North America's two anchors: the JCSEE Program Evaluation Standards' five attribute groups and the AEA Guiding Principles for Evaluators.
- [The African Evaluation Principles and Made in Africa Evaluation](https://monival.org/standards/african-evaluation-principles): AfrEA's African Evaluation Principles (2021) and the Made in Africa Evaluation agenda: origins, content, debates, and what they change in practice.
- [Evaluation Quality Assessment and Meta-Evaluation](https://monival.org/standards/evaluation-quality-assessment): How to judge an evaluation: DAC quality standards, UNEG checklists, meta-evaluation practice, and building a proportionate internal QA system.

## Site pages

- [Glossary](https://monival.org/glossary): Definitions of core M&E terms.
- [Master bibliography](https://monival.org/sources): Every source cited across the reference.
- [How we verify](https://monival.org/how-we-verify): The editorial and sourcing standard every page meets.
- [Training](https://monival.org/training): Sibasi's M&E training and capacity building.
- [About](https://monival.org/about): Who publishes this reference and why.