Survey Data, Measurement & Indicators
The unglamorous foundation every policy number rests on. Survey design and sampling, measuring welfare and inequality, composite indicators, administrative and satellite data, and the data quality, ethics, and reproducibility that decide whether the number means anything.
8
Modules
~6h 10m
Reading time
Intermediate
Level
Self-paced
Format
Syllabus
- 01→
Measurement as the foundation
Why a clean method on bad data still gives the wrong answer, and the African data gap that frames the whole problem.
~40 minModule 01 - 02→
Designing a survey
Sampling frames, questionnaire design, enumerator effects, and the census-vs-survey choice.
~50 minModule 02 - 03→
Sampling and weights
Simple, stratified, and cluster sampling, design effects, and why survey weights change the answer.
~50 minModule 03 - 04→
Measuring welfare
Consumption vs income, poverty lines and equivalence scales, and the craft of building a consumption aggregate.
~50 minModule 04 - 05→
Measuring inequality and the distribution
The Lorenz curve and the Gini, the top-incomes problem, and why survey-based inequality understates the truth.
~45 minModule 05 - 06→
Composite indicators
The HDI and the Multidimensional Poverty Index — and how the weighting and aggregation choices quietly drive the rankings.
~45 minModule 06 - 07→
Administrative and big data for policy
Call-detail records, night-lights and satellite imagery, mobile-money traces, and the privacy trade-off they force.
~45 minModule 07 - 08→
Data quality, ethics, and reproducibility
Measurement error, consent and ethics review, documentation, and the open-data and replication standard.
~45 minModule 08
How to use this course
Start with module 01 if the material is new; skip ahead if you have prior exposure. Each module is self-contained but the arc is sequential — the projects in the final module assume the toolkit from modules 1-11. Every module ends with key takeaways and a curated further-reading list with primary sources.