Stacking models of growth: a methodology for predicting the pace of progress to the education sustainable development targets using international large-scale assessments
To assess country-level progress toward these educational goals it is important to monitor trends in educational outcomes over time. The purpose of this article is to demonstrate how optimally predictive growth models can be constructed to monitor the pace of progress at which countries are moving t...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
13 February 2025
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| In: |
Psychometrika
Year: 2025, Volume: 90, Issue: 2, Pages: 658-686 |
| ISSN: | 1860-0980 |
| DOI: | 10.1017/psy.2025.2 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1017/psy.2025.2 Verlag, kostenfrei, Volltext: https://www.cambridge.org/core/journals/psychometrika/article/stacking-models-of-growth-a-methodology-for-predicting-the-pace-of-progress-to-the-education-sustainable-development-targets-using-international-largescale-assessments/D85FD94C994DB13028209C06CA323169 
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| Author Notes: | David Kaplan, Kjorte Harra, Jonas Stampka and Nina Jude |
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| 520 | |a To assess country-level progress toward these educational goals it is important to monitor trends in educational outcomes over time. The purpose of this article is to demonstrate how optimally predictive growth models can be constructed to monitor the pace of progress at which countries are moving toward (or way from) the education sustainable development goals as specified by the United Nations. A number of growth curve models can be specified to estimate the pace of progress, however, choosing one model and using it for predictive purposes assumes that the chosen model is the one that generated the data, and this choice runs the risk of “over-confident inferences and decisions that are more risky than one thinks they are” (Hoeting et al., 1999). To mitigate this problem, we adapt and apply Bayesian stacking to form mixtures of predictive distributions from an ensemble of individual models specified to predict country-level pace of progress. We demonstrate Bayesian stacking using country-level data from the Program on International Student Assessment. Our results show that Bayesian stacking yields better predictive accuracy than any single model as measured by the Kullback-Leibler divergence. Issues of Bayesian model identification and estimation for growth models are also discussed. | ||
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