Handbook of Quantitative Methods for Detecting Cheating on Tests Educational Psychology Handbook Series

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.

Editors’ Introduction

SECTION I – INTRODUCTION

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

Gregory J. Cizek and James A. Wollack

SECTION II – METHODOLOGIES FOR IDENTIFYING CHEATING ON TESTS

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 Bishopand 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,andKarla Egan

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

J. Michael Clark, William P. Skorupski, andStephen Murphy

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

Matthew Gaertner and Yuanyuan (Malena) McBride

SECTION III – THEORY, PRACTICE, AND THE FUTURE OF QUANTITATIVE DETECTION METHODS

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. SkorupskiandHoward 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

SECTION IV – CONCLUSIONS

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 andGregory J. Cizek

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.