The impact of COVID-19 on students' marks: a Bayesian hierarchical modeling approach

DOI10.1007/s40300-021-00200-1OpenAlexW3128120901MaRDI QIDQ2061392

Saeed Rahmati, Ehsan Ahmed, Lingling Jin, Shirin Boroushaki, Jabed Tomal

Publication date: 13 December 2021

Published in: Metron (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40300-021-00200-1

zbMATH Keywords

COVID-19missing value estimationscience educationhierarchical linear modeling

Mathematics Subject Classification ID

Statistics (62-XX)

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Cites Work

• Random-Effects Models for Longitudinal Data
• Coefficient alpha and the internal structure of tests
• General design Bayesian generalized linear mixed models
• Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
• Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm

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