Handbook of Quantitative Methods for Detecting Cheating on Tests

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.

• Editors’ IntroductionSECTION I – INTRODUCTIONChapter 1 – Exploring Cheating on Tests: The Context, the Concern, and the ChallengesGregory J. Cizek and James A. WollackSECTION II – METHODOLOGIES FOR IDENTIFYING CHEATING ON TESTSSection IIa – Detecting Similarity, Answer Copying, and AberranceChapter 2 – Similarity, Answer Copying, and Aberrance: Understanding the Status QuoCengiz ZopluogluChapter 3 – Detecting Potential Collusion Among Individual Examinees Using Similarity AnalysisDennis D. MaynesChapter 4 – Identifying and Investigating Aberrant Responses Using Psychometrics-Based and Machine Learning-Based ApproachesDoyoung Kim, Ada Woo, and Phil DickisonSection IIb – Detecting Preknowledge and Item CompromiseChapter 5 – Detecting Preknowledge and Item Compromise: Understanding the Status QuoCarol A. EckerlyChapter 6 – Detection of Test Collusion Using Cluster AnalysisJames A. Wollack and Dennis D. MaynesChapter 7 – Detecting Candidate Preknowledge and Compromised Content Using Differential Person and Item FunctioningLisa S. O’Leary and Russell W. SmithChapter 8 – Identification of Item Preknowledge by the Methods of Information Theory and Combinatorial OptimizationDmitry BelovChapter 9 – Using Response Time Data to Detect Compromised Items and/or PeopleKeith A. Boughton, Jessalyn Smith, and Hao RenSection IIc – Detecting Unusual Gain Scores and Test TamperingChapter 10 – Detecting Erasures and Unusual Gain Scores: Understanding the Status QuoScott Bishop and Karla EganChapter 11 – Detecting Test Tampering at the Group LevelJames A. Wollack and Carol A. EckerlyChapter 12 – A Bayesian Hierarchical Model for Detecting Aberrant Growth at the Group LevelWilliam P. Skorupski, Joe Fitzpatrick, and Karla EganChapter 13 – Using Nonlinear Regression to Identify Unusual Performance Level Classification RatesJ. Michael Clark, William P. Skorupski, and Stephen MurphyChapter 14 – Detecting Unexpected Changes in Pass Rates: A Comparison of Two Statistical ApproachesMatthew Gaertner and Yuanyuan (Malena) McBrideSECTION III – THEORY, PRACTICE, AND THE FUTURE OF QUANTITATIVE DETECTION METHODSChapter 15 – Security Vulnerabilities Facing Next Generation Accountability TestingJoseph A. Martineau, Daniel Jurich, Jeffrey B. Hauger, and Kristen HuffChapter 16 – Establishing Baseline Data for Incidents of Misconduct in the NextGen Assessment EnvironmentDeborah J. Harris and Chi-Yu HuangChapter 17 – Visual Displays of Test Fraud DataBrett P. FoleyChapter 18 – The Case for Bayesian Methods When Investigating Test Fraud William P. Skorupski and Howard WainerChapter 19 – When Numbers Are Not Enough: Collection and Use of Collateral Evidence to Assess the Ethics and Professionalism of Examinees Suspected of Test FraudMarc J. WeinsteinSECTION IV – CONCLUSIONSChapter 20 – What Have We Learned? Lorin Mueller, Yu Zhang, and Steve FerraraChapter 21 – The Future of Quantitative Methods for Detecting Cheating: Conclusions, Cautions, and RecommendationsJames A. Wollack and Gregory J. Cizek

"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, ACTThis 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