
Consultancy: Enhancing the UIS Methodology for Calculating Regional Aggregates (Re-advertised)
- Montréal, QC
- Permanent
- Temps-plein
These indicators feed into global monitoring databases and reports and are disseminated as part of the official international education statistics. The national data underpinning these aggregates are sourced from publicly available official statistics.
The quality of these aggregates is strongly influenced by the completeness of country-level data. This is particularly important for weighted averages, where countries with larger populations have a higher impact on the final aggregate. Missing data from populous countries is frequent and this can significantly impact the results. Further challenges arise from using population-based indicators that rely on population data from sources different from those used for education data, leading to occasional inconsistencies (e.g., population denominators exceeding education-related numerators in cases where this should not occur).
Moreover, as many education indicators are interrelated, calculating them in isolation may result in internal inconsistencies. The current process for producing aggregates is time-consuming, inflexible, and limits the ability to respond quickly to emerging issues or data corrections.
To address these concerns, UIS has initiated a review of its aggregation methodology and seeks to enhance the quality, reliability, transparency, and efficiency of its procedures.UIS seeks to engage a Senior Expert Consultant to develop the module on Legal Framework and Institutional Setup regarding Education Administrative data.Long DescriptionDeliverablesDeliverable 1 (duration: 1 month)
- Comprehensive review of the current UIS aggregation methodology and its implementation within the new UIS education data architecture and coding system (aligning with specifications from UIS Multi-Year Dynamic Template data collection).
- Identification of methodological weaknesses and development of a detailed set of recommendations, for programming using Python (or R) codes.
- A proposed roadmap for an enhanced aggregation methodology using Python or R codes and its implementation, including:
o Estimation and imputation approaches for indicators (at national level) with partial or missing data;
o Mechanisms to ensure consistency across interrelated indicators;
o Incorporation of confidence intervals;
o Consideration of external shocks (e.g., COVID-19, natural disasters, or socio-political crises).Deliverable 2 (duration: 1 month)
- Development of a pilot framework, including scripts (preferably in Python) and quality rating methods, for calculating aggregates focused on a core set of indicators.
- Sensitivity analysis and testing of thresholds for data coverage, quality rating of aggregates, and confidence intervals.
- Production of associated metadata for each aggregate, including:
o Confidence interval.
- Proposal for scaling the approach across all indicators produced through the UIS education data collection.
- Full-scale implementation of the new aggregation methodology.
- Complete integration with the new UIS education data processes and associated data repository systems.
- Delivery of documentation and tools supporting the new methodology.
- Advanced degree statistics, mathematics, education, economics, or a related field.
- Proven knowledge of international education statistics, including data sources, indicator methodologies, estimation techniques, and limitations;
- Extensive experience in statistical modeling, data analysis, large education databases, estimation and imputation of national-level data, and aggregation at international level;
- Excellent verbal and written communication skills in English.
- Familiarity with the International Standard Classification of Education (ISCED) and its application.
- Ability to work effectively in a remote environment.
- An updated CV
- Two samples of relevant work in statistical modeling, estimation/imputation for missing data, and aggregation, particularly in the context of international data or education.