Detecting Test Tampering Using Item Response Theory
Bibliographic Data
| ID | 20284111 |
|---|---|
| Authors | James A Wollack (0000-0002-5145-9685, University of Wisconsin, Madison, WI, USA, corresponding author), Allan S Cohen (0000-0002-8776-9378, University of Georgia, Athens, GA, USA), Carol A Eckerly (University of Wisconsin, Madison, WI, USA), Carol Eckerly (0000-0003-0121-3393, University of Wisconsin–Madison) |
| Year | 2015 |
| Volume | 75 |
| Issue | 6 |
| Pages | 931-953 |
| Publication date | 2015-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164414568716 |
| PMID | 29795847 |
| OpenAlex | W1967566865 |
| Language | EN |
| Citations received | 6 |
| References cited | 7 |
Test tampering, especially on tests for educational accountability, is an unfortunate reality, necessitating that the state (or its testing vendor) perform data forensic analyses, such as erasure analyses, to look for signs of possible malfeasance. Few statistical approaches exist for detecting fraudulent erasures, and those that do largely do not lend themselves to making probabilistic statements about the likelihood of the observations. In this article, a new erasure detection index, EDI, is developed, which uses item response theory to compare the number of observed wrong-to-right erasures to the number expected due to chance, conditional on the examinee’s ability-level and number of erased items. A simulation study is presented to evaluate the Type I error rate and power of EDI under various types of fraudulent and benign erasures. Results show that EDI with a correction for continuity yields Type I error rates that are less than or equal to nominal levels for every condition studied, and has high power to detect even small amounts of tampering among the students for whom tampering is most likely
Arithmetic · Erasure · Item response theory · Nominal level · Power (physics) · Probabilistic logic · Programming language · Psychometrics · Statistical hypothesis testing · Statistical power · Statistics · Test (biology) · Type I and type II errors · Vendor · Academic integrity and plagiarism · Advanced Statistical Methods and Models · Artificial Intelligence · Computer Science · Imbalanced Data Classification Techniques · Law · Mathematics
Refining the asymptotically correct standardization of person‐fit statistics for mixed‐format tests
Detecting Fraudulent Student Communication in a Multiple Choice Online Test Environment
An Ensemble Learning Approach Based on TabNet and Machine Learning Models for Cheating Detection in Educational Tests
The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level
Three New Methods for Analysis of Answer Changes
An introduction to statistical techniques used for detecting anomaly in test results
| Citations per year | 0.67 |
|---|---|
| Citation span | 2017 - 2026 (10) |
| Highly cited | No |