Using Nonlinear Regression to Identify Unusual Performance Level Classification Rates

Authored by: J. Michael Clark , William P. Skorupski , Stephen Murphy

Handbook of Quantitative Methods for Detecting Cheating on Tests

Print publication date:  October  2016
Online publication date:  October  2016

Print ISBN: 9781138821804
eBook ISBN: 9781315743097
Adobe ISBN: 9781317588108

10.4324/9781315743097.ch13

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Abstract

Large-scale educational testing is a high-stakes endeavor, not just for students who may need to pass a standardized test to be promoted to the next grade level or graduate, but also for educators who may experience pressures brought on by accountability expectations related to their students’ performance on these assessments. Given the ubiquity of large-scale assessments in the current K-12 educational landscape in the U.S. and the accountability pressures placed upon stakeholders, any number of individuals playing various roles in the testing process—such as test takers, teachers, proctors, educational administrators, or others—may potentially feel pressures to engage in test misconduct. Recognizing that obtaining valid test scores is a central concern for any testing program and that test misconduct represents a serious threat to test score validity (Amrein-Beardsley, Berliner, & Rideau, 2010; Cizek, 1999), it is in the best interest of educational agencies as well as their test vendors to remain vigilant for evidence of possible misconduct.

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