Course · Intermediate · Sector Analysis
WASH Analysis
Water access, quality and water point functionality against the JMP service ladders and the Sphere indicators, including the chlorination residual nobody logs consistently.
What you will be able to do
- Place a household on all three JMP service ladders, and say why an improved source is not the same as basic service
- Compute quantity, collection time and queue against the Sphere minimum standards, with the unit errors those fields carry
- Report water quality from a tested subsample without letting it borrow the access denominator
- Handle a left-censored measurement as data rather than dropping it or coercing it to zero
- Produce three defensible functionality rates from one monitoring register and state what each is about
- Separate a water point that fails every dry season from one that has been abandoned, using the visit sequence
Standards and methodologies
WASH is where this platform’s standing instruction — state the numerator, the denominator and the decision — meets a sector that has published definitions for almost everything and a habit of reporting them loosely.
The course runs on two files. The WASH household survey has appeared in three earlier courses as a cleaning problem and a denominator problem; here it is finally analysed as what it is, a service-level survey. The water point monitoring register is new, and it is the only file on the platform with repeat visits to the same asset — which is what a functionality rate needs and what a household survey structurally cannot provide.
Three results shape the course, all computed from those files.
Four fifths of households are on an improved source and barely half have basic service. 82.3% against 56.3%, and the twenty-six points between them are collection time: an improved source more than thirty minutes away is limited service, and an analysis that classifies on source type alone will report the first number and call it access.
Functionality is three numbers, not one. 74.4% of monitoring visits find a point running; 33.9% of points are running at every visit they receive; 83.3% of the people served have a working point. All three are correct and they are statements about different things.
The best-looking district is the least trustworthy number. Nord-Ouest reads 76.4%, the highest of the three, and made only 82.7% of its due visits because the roads close in the rains. If every missed visit had found a broken point it would be 63.2% — a band 13.2 points wide, against 2.6 for the district that visited nearly everything.
Both Python and R throughout. You should have done module 3 — this course assumes you can defend a denominator, read a routine figure through its reporting rate, and write an indicator reference sheet before you write the analysis.
Practice
Reading the lessons is not the same as having done the work. Each of these applies the course to a dataset it did not teach on.
- Lab · 150 minThe district that looked bestRank three districts on water point functionality, then work out that you cannot. Build the three rates, bound each one against the visits that were never made, and write the sentence that declines to rank them.
- Exercise · 60 minThe column that was not a number271 chlorine readings are the string "<0.1". Someone has coerced the column to numeric and reported 46.5% compliance. Reconstruct both figures, say which is right, and write the two sentences that go under the table.
Progress
Take it offline
The whole course as a typeset PDF — every lesson, every code example, the data dictionary and the indicator definitions. Generated from the same source as this page.
The LaTeX source ships alongside each PDF, so an organisation can rebrand the handout or fold a lesson into its own training pack.