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Leala Holcomb; Wyatte C. Hall; Stephanie J. Gardiner-Walsh; Jessica Scott – Journal of Deaf Studies and Deaf Education, 2025
This study critically examines the biases and methodological shortcomings in studies comparing deaf and hearing populations, demonstrating their implications for both the reliability and ethics of research in deaf education. Upon reviewing the 20 most-cited deaf-hearing comparison studies, we identified recurring fallacies such as the presumption…
Descriptors: Literature Reviews, Deafness, Social Bias, Test Bias
Umut Atasever; John Jerrim; Sabine Tieck – Educational Assessment, Evaluation and Accountability, 2024
Cross-national comparisons of educational achievement rely upon each participating country collecting nationally representative data. While obtaining high response rates is a key part of reaching this goal, other potentially important factors may also be at play. This paper focuses on one such issue--exclusion rates--which has received relatively…
Descriptors: International Assessment, Comparative Analysis, Cross Cultural Studies, Research Problems
Yuan Tian; Xi Yang; Suhail A. Doi; Luis Furuya-Kanamori; Lifeng Lin; Joey S. W. Kwong; Chang Xu – Research Synthesis Methods, 2024
RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two…
Descriptors: Risk, Randomized Controlled Trials, Classification, Robotics
Peter Moss; Mathias Urban – Contemporary Issues in Early Childhood, 2024
This colloquium brings information about a second cycle of OECD's International Early Learning and Well-being Study (IELS) to the early childhood community, and offers a further critique of the approach to comparative research that the IELS embodies.
Descriptors: Test Construction, Early Childhood Education, Educational Research, Research Problems
Ben Kelcey; Fangxing Bai; Amota Ataneka; Yanli Xie; Kyle Cox – Society for Research on Educational Effectiveness, 2024
We develop a structural after measurement (SAM) method for structural equation models (SEMs) that accommodates missing data. The results show that the proposed SAM missing data estimator outperforms conventional full information (FI) estimators in terms of convergence, bias, and root-mean-square-error in small-to-moderate samples or large samples…
Descriptors: Structural Equation Models, Research Problems, Error of Measurement, Maximum Likelihood Statistics