Handbook of Quantitative Methods for Detecting Cheating on Tests

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Aberrant Response Patterns
Aberrant Test
answer copying detection
Assessment
Bayesian modelling education
Brett P. Foley
Category=JMB
Category=JNDH
Cheating
Cheating Detection
Cheating Effect
Chi-Yu Huang
Cumulative Logit Regression
Daniel Jurich
Deborah J. Harris
DIF Contrast
Educational Assessment
Educational Evaluation
Educational Measurement
Educational Research
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Erasures
Evaluation Services
Examinee Pre-knowledge
Flagging Criteria
Fraud
Gregory Cizek
High Ability Examinees
Howard Wainer
Identical Incorrect Responses
IRT Model
Item Compromise
Item Parameters
Item Preknowledge
James A. Wollack
James Wollack
Jeffrey B. Hauger
Joseph A. Martineau
Kristen Huff
Lorin Mueller
machine learning assessment
Marc J. Weinstein
MLR Method
Nominal Response Model
Nonlinear Regression
Performance Standards
Person Fit Indices
Person Fit Statistic
psychometric forensics
Quantitative Methods
quantitative methods for exam security
Research Methods
response similarity analysis
Rt Model
Score Gain Analysis
Standardized Tests
statistical test integrity
Statistics
Steve Ferrara
Test Fraud
Test Security
Test Security Violations
Test Sponsors
Test Takers
Test Theory
Test Vulnerabilities
Testing
Tests
William P. Skorupski
Yu Zhang

Product details

  • ISBN 9781138821804
  • Weight: 980g
  • Dimensions: 178 x 254mm
  • Publication Date: 14 Oct 2016
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

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.