Path
Senior Monitoring & Evaluation Officer
For the officer who now signs off other people's numbers — designing the measurement system rather than filling it in, and defending it in a review where the figures are contested.
Competencies
Measurement system design
AdvancedDesign an indicator framework a whole programme reports against, including the disaggregations someone will ask for later.
Data quality assurance
AdvancedBuild the routine checks that catch a bad figure before it reaches a report, and document what each check protects against.
Reviewing an analysis
AdvancedRead someone else's analysis and find the denominator that is wrong, the exclusion that was silent, and the claim the data does not support.
Defending a number
AdvancedHold a figure under challenge from a donor, a cluster or a ministry, and concede accurately when the challenge is right.
Before you start
- The M&E Officer path, or its equivalent earned in post — you can already define an indicator and run a data quality assessment.
- Two or three reporting cycles behind you, so the failures this path names are ones you have watched happen.
Continues fromMonitoring & Evaluation OfficerThis path assumes that one rather than repeating its courses.
The route through
Stage 1
Ground the numbers
Revisit indicator definition and data quality as the person who now has to sign them off rather than the person who produces them.
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 specify an indicator framework for a multi-site programme, with numerator, denominator and disaggregation stated for every indicator.
EvidenceAn indicator reference document reviewed against the rubric.
Can review an analysis produced by someone else and identify at least one substantive methodological error.
EvidenceA written review naming the error, its effect on the conclusion, and the correction.
Can state the limitations of a figure without abandoning it.
EvidenceA limitations section that a reviewer judges neither defensive nor self-defeating.
The step from officer to senior officer is the step from producing a number to being answerable for it. That changes the work: most of the difficulty is no longer computation, it is deciding what should be counted, catching what was counted wrongly, and holding the result in a room where somebody would prefer a different answer.
This path is built around review. The three projects it recommends were all commissioned because a previously reported figure was wrong in a way nobody had noticed — a denominator that counted silent facilities as failing ones, a ranking presented as a queue, a completion rate that blamed a pathway for people who declined a referral. Reading how each was found is the training.
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 measurement work this role turns on is in the Indicators and Measurement module, and the review judgement in Statistics and Modelling — both are on the programme roadmap and neither is written yet. See the programme for what each will cover and in what order.