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Gender‐Based Differential Prediction by Curriculum Samples for College Admissions
Author(s) -
Niessen A. Susan M.,
Meijer Rob R.,
Tendeiro Jorge N.
Publication year - 2019
Publication title -
educational measurement: issues and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.158
H-Index - 52
eISSN - 1745-3992
pISSN - 0731-1745
DOI - 10.1111/emip.12266
Subject(s) - frequentist inference , curriculum , differential (mechanical device) , context (archaeology) , selection bias , test (biology) , bayesian probability , sampling (signal processing) , selection (genetic algorithm) , psychology , econometrics , statistics , computer science , machine learning , bayesian inference , mathematics , pedagogy , engineering , geography , biology , paleontology , archaeology , filter (signal processing) , computer vision , aerospace engineering
A longstanding concern about admissions to higher education is the underprediction of female academic performance by admission test scores. One explanation for these findings is selection system bias, that is, not all relevant KSAOs that are related to academic performance and gender are included in the prediction model. One solution to this problem is to include these omitted KSAOs in the prediction model, many of these KSAOs are 'noncognitive' and “hard‐to‐measure” skills in a high‐stakes context. An alternative approach to capture relevant KSAOs is using representative performance samples. We examined differential prediction of first year‐ and third year academic performance by gender based on a curriculum‐sampling test that was designed as a small‐scale simulation of later college performance. In addition, we examined differential prediction using both frequentist and Bayesian analyses. Our results showed no differential prediction or small female underprediction when using the curriculum‐sampling tests to predict first year GPA, and no differential prediction for predicting third year GPA. In addition, our results suggest that more comprehensive curriculum samples may show less differential prediction. We conclude that curriculum sampling may offer a practically feasible method that yields minimal differential prediction by gender in high‐stakes operational selection settings.