A Bayesian model for estimating Sustainable Development Goal indicator 4.1.2: School completion rates

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-09-25 DOI:10.1111/rssc.12595

Ameer Dharamshi, Bilal Barakat, Leontine Alkema, Manos Antoninis

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引用次数: 3

Abstract

Estimating school completion is crucial for monitoring Sustainable Development Goal (SDG) 4 on education. The recently introduced SDG indicator 4.1.2, defined as the percentage of children aged 3–5 years above the expected completion age of a given level of education that have completed the respective level, differs from enrolment indicators in that it relies primarily on household surveys. This introduces a number of challenges including gaps between survey waves, conflicting estimates, age misreporting and delayed completion. We introduce the Adjusted Bayesian Completion Rates (ABCR) model to address these challenges and produce the first complete and consistent time series for SDG indicator 4.1.2, by school level and sex, for 164 countries. Validation exercises indicate that the model appears well-calibrated and offers a meaningful improvement over simpler approaches in predictive performance. The ABCR model is now used by the United Nations to monitor completion rates for all countries with available survey data.

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估算可持续发展目标指标4.1.2:学校完成率的贝叶斯模型

估计学校完成情况对于监测关于教育的可持续发展目标4至关重要。最近引入的可持续发展目标指标4.1.2定义为超过预期完成某一特定教育水平的3-5岁儿童完成相应教育水平的百分比，它与入学率指标不同，因为它主要依赖于住户调查。这带来了许多挑战，包括调查浪潮之间的差距、相互矛盾的估计、年龄错误报告和延迟完成。我们引入了调整贝叶斯完成率(ABCR)模型来应对这些挑战，并为164个国家的可持续发展目标指标4.1.2制作了第一个完整和一致的时间序列，按学校水平和性别分列。验证练习表明，该模型似乎经过了很好的校准，并在预测性能方面提供了比更简单的方法有意义的改进。联合国现在使用ABCR模式来监测所有拥有调查数据的国家的完成率。

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来源期刊

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.