Gender‐Based Differential Prediction by Curriculum Samples for College Admissions. Issue 3 (3rd June 2019)

• Record Type: Journal Article
• Authors: Niessen, A. Susan M.; Meijer, Rob R.; Tendeiro, Jorge N.
• Is Part Of: Educational measurement. Volume 38:Issue 3(2019)
• Page Start: 33
• Page End: 45
• Publication Date: 2019-06-03
• Subjects: Bayesian analyses -- college admission -- curriculum sampling -- differential prediction -- high‐stakes assessment; Educational tests and measurements -- Periodicals; Educational tests and measurements -- United States -- Periodicals; 371.262
• DOI: 10.1111/emip.12266
• Languages: English
• ISSNs: 0731-1745

Abstract

Abstract: 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.