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Direct linkFrancis Huang; Brian Keller – Large-scale Assessments in Education, 2025
Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and…
Descriptors: Research Problems, Data Analysis, Statistical Bias, International Assessment
Kaitlyn Coburn; Kris Troy; Carly A. Busch; Naomi Barber-Choi; Kevin M. Bonney; Brock Couch; Marcos E. García-Ojeda; Rachel Hutto; Lauryn Famble; Matt Flagg; Tracy Gladding; Anna Kowalkowski; Carlos Landaverde; Stanley M. Lo; Kimberly MacLeod; Blessed Mbogo; Taya Misheva; Andy Trinh; Rebecca Vides; Erik Wieboldt; Cara Gormally; Jeffrey Maloy – CBE - Life Sciences Education, 2025
Trans* and genderqueer student retention and liberation is integral for equity in undergraduate education. While STEM leadership calls for data-supported systemic change, the erasure and othering of trans* and genderqueer identities in STEM research perpetuates cisnormative narratives. We sought to characterize how sex and gender data are…
Descriptors: LGBTQ People, Transgender People, Disproportionate Representation, Educational Research
Philip E. Kearney; Niamh Curran; Frank J. Nugent – Journal of Motor Learning and Development, 2025
Manipulation checks are an essential component of quality experimental design in motor learning. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, this methodological systematic review examined the utilization of manipulation checks in focus of attention research. Seventy-eight protocols from four…
Descriptors: Attention Control, Attention Span, Motor Development, Psychomotor Skills
Jiang Li; Chen Zhu; Mark Goh – Research Evaluation, 2025
Data Envelopment Analysis (DEA) is a widely adopted non-parametric technique for evaluating R&D performance. However, traditional DEA models often struggle to provide reliable solutions in the presence of data uncertainty. To address this limitation, this study develops a novel robust super-efficiency DEA approach to evaluate R&D…
Descriptors: Foreign Countries, Research and Development, COVID-19, Pandemics
