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Yannick Rothacher; Carolin Strobl – Journal of Educational and Behavioral Statistics, 2024
Random forests are a nonparametric machine learning method, which is currently gaining popularity in the behavioral sciences. Despite random forests' potential advantages over more conventional statistical methods, a remaining question is how reliably informative predictor variables can be identified by means of random forests. The present study…
Descriptors: Predictor Variables, Selection Criteria, Behavioral Sciences, Reliability
Thomas Muecke; Arya Rao; Hugo Walker; Joshua Tinnion; Daniel Jesudason; Stephen Bacchi; Robert Casson; Weng Onn Chan – Discover Education, 2024
Successful entrance into specialty training represents a pivotal stage in the careers of medical officers. Selection for entrance into specialty training programs may encompass criteria including research experience, regional exposure, clinical experience, professional achievements, diversity, equity and inclusion factors, and extracurricular…
Descriptors: Specialists, Medical Education, Surgery, Selection Criteria
Joao M. Souto-Maior; Kenneth A. Shores; Rachel E. Fish – Annenberg Institute for School Reform at Brown University, 2025
Whether selection processes contribute to group-level disparities or merely reflect pre-existing inequalities is an important societal question. In the context of observational data, researchers, concerned about omitted-variable bias, assess selection-contributing inequality via a kitchen-sink approach, comparing selection outcomes of…
Descriptors: Control Groups, Predictor Variables, Correlation, Selection Criteria