Predicting Future Academic Success: How a Bayesian Approach Can Be of Help in Analyzing and Interpreting Admission Test Scores

Jorge N. Tendeiro, A. Susan M. Niessen, Daniela R. Crisan, Rob R. Meijer

Research output: Book/ReportReport

Abstract

The aim of this study was twofold: First, we investigated whether scores on an admission test administered in proctored and unproctored environments led to similar predictions of future academic success. Second, we explored how Bayesian modeling can be of help in interpreting admission-testing data. Results showed that the two modes of administering an admission test did not require the use of different models for predicting academic success, and that Bayesian modeling provides a very useful and easy-to-interpret framework for predicting future academic success.
Original languageEnglish
Publication statusAccepted/In press - 2019

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