Four stages, each with something agreed in writing before the next begins. The method is proportionate to the decision: defensible enough to publish, practical enough to deliver on time.
Inception discussions, theory of change review, evaluation questions and indicator definitions agreed in writing.
Mixed-method design, transparent sampling, instrument development and enumerator training in local languages.
Digital data collection, supervisory back-checks, consent protocols and daily data quality review.
Validation with stakeholders, decision-ready reporting, and support to act on the recommendations.
Findings feed back into design: indicators, instruments and sampling are revised for the next round, so each assignment strengthens the client's own measurement system rather than producing a report that sits on a shelf.
PMEye designs and implements mixed-method research across humanitarian, development and public sector programmes. Instruments are selected for the evaluation question, the population and the operating environment, not applied as a template.
Most assignments combine structured measurement with qualitative depth, so that findings are both representative and explainable.
Establishes what is happening, for how many people, and whether it has shifted since the last round.
Explains why the numbers look the way they do, and surfaces what a survey instrument was never going to ask.
Puts the people a programme is meant to serve in the position of assessing it, rather than only answering about it.
Locates services and populations in real geography, and shortens the distance between a completed interview and a usable dataset.
Instruments are built as digital forms and deployed on tablets or phones. The platform is chosen to fit the reporting environment of the commissioning organisation and the connectivity of the assignment area.
Platforms available and selected according to assignment requirements.
Enumerators keep working where there is no signal; submissions sync when connectivity returns.
Each submission carries a location and timestamp, so an interview can be verified after the fact.
Skip logic, range checks and mandatory fields stop errors before they reach the dataset.
Team leaders see submission counts and quality flags while the team is still in the field.
Data arrives analysis-ready, shortening the gap between the last interview and the first finding.
Data quality is a sequence of decisions taken during fieldwork, not a check applied to a finished dataset. Every round runs through the same steps.
Instrument design
Translation and back-translation
Enumerator training
Pilot and instrument revision
Live field supervision
Back-checks and spot checks
Daily data review
Reaching women is a design decision, not a logistical afterthought. We build female enumerator teams, run female-only focus groups, and hold them in places and at times when participants can actually attend and speak freely. Consent is taken in the language of the interview, and results are disaggregated by sex so that differences show up in the findings rather than being averaged away. Without this, half the population answers through someone else.
Where the sample is hardest to reach, the evidence is usually thinnest, and the decisions that depend on it are the most consequential.
Mountain districts, riverine and island settlements, and areas without road access. Access is planned into the design, not improvised in the field.
Teams recruited and trained locally, with female enumerators for women-centred research, working in Urdu, Pashto, Swahili, Chichewa and the other languages of the communities we survey.
Documented protocols for informed consent, safeguarding and data protection, applied on every assignment.
Fieldwork quality is handled in the field, step by step. These two checks sit after it, between a finished dataset and a published finding. They are the reason our limitations sections are honest.
Findings tested against the data by someone who did not produce them, with limitations stated rather than smoothed over.
Stakeholder workshops to test interpretation and agree what should happen next, including with the communities that provided the data.
Digital collection and structured analysis shorten timelines considerably. They do not change who is answerable for a finding.
Where automated analysis is used, a specialist reviews the output before it informs a conclusion. Data-protection protocols govern what is collected, how it is stored and who can see it. If a finding cannot be traced back to the evidence that produced it, it does not go in the report.
Analysis is kept separate from delivery, and findings are tested by a second specialist who did not produce them. Where we have also implemented, we say so plainly in the report rather than leaving the reader to discover it.
We report it. Limitations are stated rather than smoothed over, and interpretation is tested at the validation workshop with the people who took part. A report that only confirms what was assumed has not earned its cost.
That depends on instrument sign-off and enumerator recruitment in the district concerned. We will give you a realistic date at proposal stage, including the training window, rather than a date that assumes everything goes right.
Yes. It is a substantial part of our practice. Access planning, local recruitment and documented safety protocols are built into the design from the start.
Yes. Instruments, sampling documentation and, where relevant, the data system are handed over so your team can run the next round. Structured knowledge transfer is part of the assignment, not an extra.
Design, team, timeline and price. Or, if the method is not settled yet, options scoped against the decision you face.