Test Fraud

1. Introduction Neal Kingston and Amy Clark 2. A Brief History of Research on Test Fraud Detection and Prevention Amy Clark and Neal Kingston 3. Cheating: Some Ways to Detect it Badly Howard Wainer Part 1: Similarities in Responses 4. Relationships of Examinee Pair Characteristics and Item Response Similarity Jeff Allen 5. A Parametric Approach to Detect a Disproportionate Number of Identical Item Responses on a Test Leonardo S. Sotaridona, Arianto Wibowo, and Irene Hendrawan 6. Detection of Non-Independent Test Taking by Similarity Analysis Dennis Maynes Part 2: Macro Level Cheating 7. Local Outlier Detection in Data Forensics: Data Mining Approach to Flag Unusual Schools Mayuko Simon 8. Macro Level Systems of Statistical Evidence Indicative of Cheating Michael Chajewski, YoungKoung Kim, Judit Antal, and Kevin Sweeney 9. A Bayesian Hierarchical Linear Modeling Approach for Detecting Cheating and Aberrance William Skorupski and Karla Egan Part 3: Answer Changing Behavior 10. Patterns of Erasure Behavior for a Large-Scale Assessment Andrew A. Mroch, Yang Lu, Chi-Yu Huang, and Deborah J. Harris 11. AYP consequences and Erasure Behavior Vincent Primoli 12. An Exploration of Answer Changing Behavior on a Computer-Based High-Stakes Achievement Test Gail C. Tiemann and Neal M. Kingston Part 4: Detection of Aberrant Responses 13. Identifying Non-Effortful Student Behavior on Adaptive Tests: Implications for Test Fraud Detection Steven L. Wise, Lingling Ma, and Robert A. Theaker 14. A Method for Measuring Performance Inconsistency by Using Score Differences Dennis Maynes Part 5: Multiple Methods 15. Data Forensics: A Compare-and-Contrast Analysis of Multiple Methods Christie Plackner and Vincent Primoli 16. Using Multiple Methods to Detect Aberrant Data Karla Egan and Jessalyn Smith 17. Test Security for Multistage Tests: A Quality Control Perspective Charles Lewis, Yi-Hsuan Lee and Alina A. von Davier