Handbook of Quantitative Methods for Detecting Cheating on Tests  book cover
1st Edition

Handbook of Quantitative Methods for Detecting Cheating on Tests

ISBN 9781138821811
Published September 30, 2016 by Routledge
444 Pages 71 B/W Illustrations

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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

Editors’ Introduction


Chapter 1 – Exploring Cheating on Tests: The Context, the Concern, and the Challenges

Gregory J. Cizek and James A. Wollack


Section IIa – Detecting Similarity, Answer Copying, and Aberrance

Chapter 2 – Similarity, Answer Copying, and Aberrance: Understanding the Status Quo

Cengiz Zopluoglu

Chapter 3 – Detecting Potential Collusion Among Individual Examinees Using Similarity Analysis

Dennis D. Maynes

Chapter 4 – Identifying and Investigating Aberrant Responses Using Psychometrics-Based and Machine Learning-Based Approaches

Doyoung Kim, Ada Woo, and Phil Dickison

Section IIb – Detecting Preknowledge and Item Compromise

Chapter 5 – Detecting Preknowledge and Item Compromise: Understanding the Status Quo

Carol A. Eckerly

Chapter 6 – Detection of Test Collusion Using Cluster Analysis

James A. Wollack and Dennis D. Maynes

Chapter 7 – Detecting Candidate Preknowledge and Compromised Content Using Differential Person and Item Functioning

Lisa S. O’Leary and Russell W. Smith

Chapter 8 – Identification of Item Preknowledge by the Methods of Information Theory and Combinatorial Optimization

Dmitry Belov

Chapter 9 – Using Response Time Data to Detect Compromised Items and/or People

Keith A. Boughton, Jessalyn Smith, and Hao Ren

Section IIc – Detecting Unusual Gain Scores and Test Tampering

Chapter 10 – Detecting Erasures and Unusual Gain Scores: Understanding the Status Quo

Scott Bishop and Karla Egan

Chapter 11 – Detecting Test Tampering at the Group Level

James A. Wollack and Carol A. Eckerly

Chapter 12 – A Bayesian Hierarchical Model for Detecting Aberrant Growth at the Group Level

William P. Skorupski, Joe Fitzpatrick, and Karla Egan

Chapter 13 – Using Nonlinear Regression to Identify Unusual Performance Level Classification Rates

J. Michael Clark, William P. Skorupski, and Stephen Murphy

Chapter 14 – Detecting Unexpected Changes in Pass Rates: A Comparison of Two Statistical Approaches

Matthew Gaertner and Yuanyuan (Malena) McBride


Chapter 15 – Security Vulnerabilities Facing Next Generation Accountability Testing

Joseph A. Martineau, Daniel Jurich, Jeffrey B. Hauger, and Kristen Huff

Chapter 16 – Establishing Baseline Data for Incidents of Misconduct in the NextGen Assessment Environment

Deborah J. Harris and Chi-Yu Huang

Chapter 17 – Visual Displays of Test Fraud Data

Brett P. Foley

Chapter 18 – The Case for Bayesian Methods When Investigating Test Fraud

William P. Skorupski and Howard Wainer

Chapter 19 – When Numbers Are Not Enough: Collection and Use of Collateral Evidence to Assess the Ethics and Professionalism of Examinees Suspected of Test Fraud

Marc J. Weinstein


Chapter 20 – What Have We Learned?

Lorin Mueller, Yu Zhang, and Steve Ferrara

Chapter 21 – The Future of Quantitative Methods for Detecting Cheating: Conclusions, Cautions, and Recommendations

James A. Wollack and Gregory J. Cizek

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Gregory J. Cizek is the Guy B. Phillips Distinguished Professor of Educational Measurement and Evaluation in the School of Education at the University of North Carolina, Chapel Hill, USA.

James A. Wollack is Professor of Quantitative Methods in the Educational Psychology Department and Director of Testing and Evaluation Services at the University of Wisconsin, Madison, USA.


"Today, cheating increasingly presents ever-changing challenges to the integrity of test results used for admissions, graduation, certification, professional licensure, and accountability. Cizek and Wollack are two of the most recognized and cited experts on educational test security, and the Handbook of Quantitative Methods for Detecting Cheating on Tests provides the most comprehensive treatment of statistical methods for detection that simply must be incorporated into any large-scale assessment program used for high-stakes decisions."

--Wayne Camara, Senior Vice President, Research, ACT

This edited volume has taken the importance of test security in test validation to a different level. It reflects the maturity of the field of cheating detection, whereby statistical probabilities are no longer presented as inferential leaps into vague, colluded, remote chances of cheating behavior; rather, they are presented using precise empirical evidence that identifies specific cheating behaviors on which one can act. The authors bring together comprehensive knowledge on increasing data forensics and methodologies alongside legally presentable evidence to help reduce the fraudulent use of test results. The book will sit atop my bookshelf for years to come.

--Ardeshir Geranpayeh, Head of Automated Assessment & Learning at Cambridge English Language Assessment, University of Cambridge, UK