http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Handbook of quantitative methods for detecting cheating on tests
Exploring Cheating on Tests : The Context, the Concern, and the Challenges / Gregory J. Cizek and James A. Wollack -- Similarity, Answer Copying, and Aberrance : Understanding the Status Quo / Cengiz Zopluoglu -- Detecting Potential Collusion among Individual Examinees using Similarity Analysis / Dennis D. Maynes -- Identifying and Investigating Aberrant Responses Using Psychometrics-Based and Machine Learning-Based Approaches / Doyoung Kim, Ada Woo, and Phil Dickison -- Detecting Preknowledge and Item Compromise : Understanding the Status Quo / Carol A. Eckerly -- Detection of Test Collusion using Cluster Analysis / James A. Wollack -- Detecting Candidate Pre-knowledge and Compromised Content using Differential Person and Item Functioning / Lisa O'Leary and Russell Smith -- Identification of Item Preknowledge by the Methods of Information Theory and Combinatorial Optimization / Dmitry Belov -- Using Response Time Data to Detect Compromised Items and/or People / Keith A. Boughton, Jessalyn Smith and Hao Ren -- Detecting Erasures and Unusual Gain Scores : Understanding the Status Quo / Scott Bishop and Karla Egan -- Detection of Test Tampering at the Group Level / James A. Wollack and Carol A. Eckerly -- A Bayesian Hierarchical Linear Model for Detecting Aberrant Growth at the Group Level / William P. Skorupski, Joe Fitzpatrick and Karla Egan -- Using Nonlinear Regression to Identify Unusual Performance Level Classification Rates / J. Michael Clark, William P. Skorupski and Stephen Murphy -- Detecting Unexpected Changes in Pass Rates : A Comparison of Two Statistical Approaches / Matthew N. Gaertner and Yuanyuan Z. McBride -- Security Vulnerabilities Facing Next Generation Accountability Testing / Joseph Martineau, Daniel Jurich, Jeffrey B. Hauger, and Kristen Huff -- Establishing Baseline Data for Incidents of Misconduct in the NextGen Assessment Environment / Deborah J. Harris and Chi-Yu Huang -- Visual Displays of Test Fraud Data / Brett P. Foley -- The Case for Bayesian Methods when Investigating Test Fraud / William P. Skorupski and Howard Wainer -- 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 -- What We Have Learned? / Lorin Mueller, Yu Zhang and Steve Ferrara -- The Future of Quantitative Methods for Detecting Cheating : Conclusions, Cautions, and Recommendations / James A. Wollack and Gregory J. Cizek.
edited by Gregory J. Cizek and James A. Wollack.
Includes bibliographical references and index.