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The Handbook of Quantitative Methods for Detecting Cheating on Tests is a comprehensive book that describes the variety of ways people cheat and the quantitative methods that have been developed to detect and combat them.
The Handbook of Quantitative Methods for Detecting Cheating on Tests is a comprehensive book that describes the variety of ways people cheat and the quantitative methods that have been developed to detect and combat them.
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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.
Inhaltsangabe
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 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 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. 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 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 and Gregory J. Cizek
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 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 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. 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 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 and Gregory J. Cizek
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