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Denise LIEVESLEY

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

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Denise LIEVESLEY

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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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