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Handbook of Quantitative Methods for Detecting Cheating on Tests

Edited by: Gregory J. Cizek , James A. Wollack

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

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

10.4324/9781315743097
 Cite  Marc Record

Book description

The rising reliance on testing in American education and for licensure and certification has been accompanied by an escalation in cheating on tests at all levels. Edited by two of the foremost experts on the subject, the Handbook of Quantitative Methods for Detecting Cheating on Tests offers a comprehensive compendium of increasingly sophisticated data forensics used to investigate whether or not cheating has occurred. Written for practitioners, testing professionals, and scholars in testing, measurement, and assessment, this volume builds on the claim that statistical evidence often requires less of an inferential leap to conclude that cheating has taken place than do other, more common sources of evidence.

This handbook is organized into sections that roughly correspond to the kinds of threats to fair testing represented by different forms of cheating. In Section I, the editors outline the fundamentals and significance of cheating, and they introduce the common datasets to which chapter authors' cheating detection methods were applied. Contributors describe, in Section II, methods for identifying cheating in terms of improbable similarity in test responses, preknowledge and compromised test content, and test tampering. Chapters in Section III concentrate on policy and practical implications of using quantitative detection methods. Synthesis across methodological chapters as well as an overall summary, conclusions, and next steps for the field are the key aspects of the final section.

Table of contents

Prelims Download PDF
Chapter  1:  Exploring Cheating on Tests Download PDF
Chapter  2:  Similarity, Answer Copying, and Aberrance Download PDF
Chapter  3:  Detecting Potential Collusion among Individual Examinees using Similarity Analysis Download PDF
Chapter  4:  Identifying and Investigating Aberrant Responses using Psychometrics-Based and Machine Learning-Based Approaches  Download PDF
Chapter  5:  Detecting Preknowledge and Item Compromise Download PDF
Chapter  6:  Detection of Test Collusion using Cluster Analysis Download PDF
Chapter  7:  Detecting Candidate Preknowledge and Compromised Content using Differential Person and Item Functioning Download PDF
Chapter  8:  Identification of Item Preknowledge by the Methods of Information Theory and Combinatorial Optimization Download PDF
Chapter  9:  Using Response Time Data to Detect Compromised Items and/or People Download PDF
Chapter  10:  Detecting Erasures and Unusual Gain Scores Download PDF
Chapter  11:  Detecting Test Tampering at the Group Level Download PDF
Chapter  12:  A Bayesian Hierarchical Model for Detecting Aberrant Growth at the Group Level Download PDF
Chapter  13:  Using Nonlinear Regression to Identify Unusual Performance Level Classification Rates Download PDF
Chapter  14:  Detecting Unexpected Changes in Pass Rates Download PDF
Chapter  15:  Security Vulnerabilities Facing Next Generation Accountability Testing Download PDF
Chapter  16:  Establishing Baseline Data for Incidents of Misconduct in the Nextgen Assessment Environment Download PDF
Chapter  17:  Visual Displays of Test Fraud Data Download PDF
Chapter  18:  The Case for Bayesian Methods when Investigating Test Fraud Download PDF
Chapter  19:  When Numbers are not Enough Download PDF
Chapter  20:  What Have We Learned? Download PDF
Chapter  21:  The Future of Quantitative Methods for Detecting Cheating Download PDF
Contributors Download PDF
Appendix_B Download PDF
Appendix_C Download PDF
Appendix_A Download PDF
Index Download PDF
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