Path
Monitoring & Evaluation Manager
For the manager accountable for a measurement function rather than an analysis — what to standardise, what to let teams decide, and how to tell whether the data your programme produces is worth its cost.
Competencies
Measurement strategy
AdvancedDecide what a programme should measure at all, and defend leaving something unmeasured when the cost of measuring it exceeds its value.
Standardisation
AdvancedChoose which definitions, thresholds and formats are fixed across teams and which are left to the site, and write the difference down.
Evaluation commissioning
AdvancedSpecify an evaluation well enough that its findings will be usable, and recognise a design that cannot answer the question asked.
Reading analysis critically
IntermediateInterrogate an analysis you did not produce, without needing to reproduce it, by asking the questions its method must answer.
Before you start
- The Senior M&E Officer path, or its equivalent — you have owned a reporting cycle from raw export to submitted narrative.
- A team, or the prospect of one. Much of this path is about reviewing work you did not write and asking the question that finds the error.
Continues fromSenior Monitoring & Evaluation OfficerThis path assumes that one rather than repeating its courses.
The route through
Stage 1
Ground the numbers
Hold the indicator definitions your team reports against, so a disputed figure in a steering meeting is settled by the definition rather than by seniority.
Stage 2
Make the data trustworthy
Turn a raw export into a table you would defend line by line, and hand over a cleaning log that answers the auditor's question before it is asked.
Stage 3
Put the files together
Assemble several exports into one analysis table you would defend column by column, with every join proved, every grain stated and every denominator sourced.
Stage 4
Check it like an auditor
Assess your own data before a donor does — five dimensions with measures, a recount against the source, and a report where every finding has an owner and a date.
Stage 5
Define what you report
Write indicator definitions two analysts compute the same way, defend the denominator, and set a baseline and target that survive a mid-term review.
Stage 6
Put an interval on it
Analyse a cluster survey the way its design requires — weights from the frame, a measured design effect, and an interval that says what the sample can and cannot settle.
Stage 7
Know where it came from
Read a routine reporting system as the database it is, pull an extract you can point at months later, and answer what was counted for any figure it produces.
Stage 8
Apply it to nutrition
Take the methods into one sector — WHO growth standards, SMART plausibility, the IPC phases and the Sphere performance thresholds, on a survey and a treatment register.
Stage 9
Apply it to public health
Rates with person-time denominators, treatment cascades, coverage three ways, and an outbreak line list turned into a curve, attack rates and a case fatality you can defend.
Stage 10
Apply it to WASH
The JMP service ladders and the Sphere minimums on a household survey, then a monitoring register with repeat visits that turns one functionality rate into three and bounds each against the rounds nobody drove.
Stage 11
Apply it to food security
FCS, HHS, rCSI and the livelihood coping module built from raw components and found to disagree by a factor of seven, then a price series whose seasonality dwarfs the programme effect and the evidence table that reports both.
Stage 12
Apply it to protection
The analyses you must decline to publish, alongside the ones that matter — a consent-gated referral pathway, a nineteen-point equity gap located at a specific gate, and a caseload that explains two other tables.
Stage 13
Apply it to education
Gross against net enrolment on a projected denominator, two attendance numbers twenty-six points apart, a cohort through promotion and repetition, and two assessment rounds whose instruments differ.
Stage 14
Say how sure you are
An interval on every proportion, the right test for a comparison, an effect size beside every p-value, and the count of comparisons that turns two striking schools back into noise.
Stage 15
Model more than one thing at once
A coefficient is a comparison — which one, between which units, adjusted for what. An odds ratio your reader will misread, a covariate that removes 42% of the effect, and a model that explains three per cent and settles a targeting decision.
Stage 16
Say what caused it
A seven-point gain that is entirely the school year, a comparison group imbalanced on every characteristic measured, and the minimum detectable effect that decided the answer before any data existed.
Stage 17
Put it in front of them
The mark the comparison implies, an interval that stops a ranking, a palette that already means something to this audience, and a figure generated from the dataset so the chart and the sentence cannot drift.
Stage 18
Make it rerunnable
Raw data read-only and code the only thing edited, an environment pinned so a colleague's laptop gives your numbers, checks that stop the pipeline rather than producing a plausible wrong one, and a handover a successor can act on.
Stage 19
Put it in front of the decision
A page that answers one question rather than twenty, the definition panel that stops the monthly argument, and the report whose findings, limitations and recommendation survive being read separately.
Recommended projects
How the skill is assessed
Can write a measurement plan that states, for each indicator, the decision it informs and who takes that decision.
EvidenceA measurement plan in which every indicator has a named decision and a named owner.
Can identify an indicator a programme collects and does not use, and argue for dropping it.
EvidenceA written case for removal, with the collection cost and the consequence of not having it.
Can specify the terms of reference for an evaluation against a stated decision.
EvidenceTerms of reference reviewed against the OECD DAC criteria.
A manager is rarely the person who computes anything. The judgements that matter are earlier and coarser: whether an indicator is worth its collection cost, whether a threshold should be the same in every district, whether an evaluation design can answer the question it has been given.
Those judgements are technical even when the work is not. A manager who cannot tell whether a targeting threshold was chosen for need or for budget will approve the one that was chosen for budget — which is why the food security project in this path spends its technical document on exactly that question.
What this path still needs
Three courses are published: Data Analysis Foundations, Python for Programme Data and R and the Tidyverse for Programme Data. The commissioning and evaluation-design content this role needs sits in Statistics and Modelling and Communication and Delivery on the programme roadmap; neither is written yet. See the programme.