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Enhancing Education Data for Effective Policy Making and Monitoring EFA Goals

This document discusses the vital role of quality education data in formulating evidence-based policies and monitoring the Education for All (EFA) goals. It outlines various data sources, including administrative records, surveys, and censuses, emphasizing the importance of understanding their merits and limitations. The document highlights the need for regular data collection and comparative assessments to improve data relevance and communication between data producers and users. Strategies for enhancing data quality, including simplifying processes and building capacity, are also detailed to support national policy objectives while meeting international demands.

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Enhancing Education Data for Effective Policy Making and Monitoring EFA Goals

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  1. Denise LIEVESLEY UNESCO Institute for Statistics Institut de statistique de l’UNESCO EFA and statistics

  2. Statistics in the EFA process • Formulation of evidence-based policies at national level • Monitoring of EFA goals and other performance indicators at national and international levels • Advocacy in order to mobilise support and resources

  3. Data sources • Administrative data • Census data • School surveys • Surveys of students • Household surveys

  4. Important to understand merits and limitations/ likely deficiencies of the data from different sources • Critical not to reply on too small a number of indicators

  5. Broad timetable - education data

  6. Can be combined to provide estimates in all dimensions In-depth data on literacy SKILLS and people’s background In-depth data on higher literacy levels REGULAR data collection LAMP Household surveys ALL, IALS Large population COVERAGE Data on students PISA, SACMEQ, PIRLS Censuses Comparative cross-national assessement surveys Declarations & mini-tests Ad hoc literacy assessment surveys Literacy tests as part of part wider evaluations Programme evaluations Individual diagnostics Evaluating programme effectiveness Literacy Measures

  7. Improving the policy relevance of data • Improving the dialogue between users and producers of data within countries need to understand the policies, improve the communication skills of statisticians, user committees, building communities of intermediaries • Balancing the country and cross-national comparative data needs ensuring that international demands do not distort national agendas, involve national policy makers in the decisions about international data

  8. Data quality • reliability • validity • timeliness – currency and punctuality • comparability between and within countries • consistency over time • can be disaggregated • accessible and interpretable • policy relevant • affordable and cost effective

  9. How can we assess data quality ? • Usual statistical ways - consistency, face validity, within range etc • Triangulation with other sources especially surveys and disaggregated data • Feedback from users, those in the field, especially n.g.o.s • Supportive relationships with statisticians who supply the data • Good contextual information • Comparisons with other countries

  10. How do we improve data quality ? • Improve processes • Simplify data collection • Ensure resources and expertise for data collection (ie build capacity) • Understand incentives throughout the system • Get data used • Raise the profile of data

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