Three New Methods for Analysis of Answer Changes
Bibliographic Data
| ID | 20284757 |
|---|---|
| Authors | Sandip Sinharay (0000-0003-4491-8510, Pacific Metrics Corporation, Monterey, CA, USA, corresponding author), Matthew Johnson (0000-0002-7295-2663, Columbia University), Matthew S Johnson (0000-0001-6029-896X, Columbia University, New York, NY, USA) |
| Year | 2017 |
| Volume | 77 |
| Issue | 1 |
| Pages | 54-81 |
| Publication date | 2017-01-01 |
| 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 | 10.1177/0013164416632287 |
| PMID | 29795903 |
| OpenAlex | W2327223700 |
| Language | EN |
| Citations received | 4 |
| References cited | 20 |
In a pioneering research article, Wollack and colleagues suggested the “erasure detection index” (EDI) to detect test tampering. The EDI can be used with or without a continuity correction and is assumed to follow the standard normal distribution under the null hypothesis of no test tampering. When used without a continuity correction, the EDI often has inflated Type I error rates. When used with a continuity correction, the EDI has satisfactory Type I error rates, but smaller power compared with the EDI without a continuity correction. This article suggests three methods for detecting test tampering that do not rely on the assumption of a standard normal distribution under the null hypothesis. It is demonstrated in a detailed simulation study that the performance of each suggested method is slightly better than that of the EDI. The EDI and the suggested methods were applied to a real data set. The suggested methods, although more computation intensive than the EDI, seem to be promising in detecting test tampering
Algorithm · Data mining · Econometrics · Erasure · Error detection and correction · Null (SQL) · Null distribution · Null hypothesis · Set (abstract data type) · Statistical hypothesis testing · Statistical power · Statistics · Test (biology) · Test statistic · Type I and type II errors · Computer Science · Imbalanced Data Classification Techniques · Mathematics · Sports Analytics and Performance · Statistical Methods in Clinical Trials
Linear Statistical Inference and its Applications
Bayesian Data Analysis
Weighted Likelihood Estimation of Ability in Item Response Theory
Detecting Test Tampering Using Item Response Theory
Biweight Estimates of Latent Ability
The Empirical Power and Type I Error Rates of the GBT and ω Indices in Detecting Answer Copying on Multiple-Choice Tests
Estimating item parameters and latent ability when responses are scored in two or more nominal categories
| Citations per year | 0.8 |
|---|---|
| Citation span | 2021 - 2025 (5) |
| Highly cited | No |