James A Wollack
Biographic Data
| ID | 9252831 |
|---|---|
| NAME | James A Wollack |
| GIVEN NAMES | James A |
| FAMILY NAME | Wollack |
| SIGNATURE | WOLLACK J A |
| AFFILIATIONS | University of Wisconsin–Madison |
| ORCID | 0000-0002-5145-9685 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2015 |
| H-INDEX | 0 |
Detecting Test Tampering Using Item Response Theory
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…
On the Use of Nonparametric Item Characteristic Curve Estimation Techniques for Checking Parametric Model Fit
The purpose of this study was to assess the model fit of a 2PL through comparison with the nonparametric item characteristic curve (ICC) estimation procedures. Results indicate that three nonparametric procedures implemented produced ICCs that are similar to that of the 2PL for items simulated to fit the 2PL. However for misfitting items, especially nonmonotone items, the greatest difference is between the 2PL and kernel smoothing procedures. In …
No prominent works on this page.
On the Use of Nonparametric Item Characteristic Curve Estimation Techniques for Checking Parametric Model Fit
The purpose of this study was to assess the model fit of a 2PL through comparison with the nonparametric item characteristic curve (ICC) estimation procedures. Results indicate that three nonparametric procedures implemented produced ICCs that are similar to that of the 2PL for items simulated to fit the 2PL. However for misfitting items, especially nonmonotone items, the greatest difference is between the 2PL and kernel smoothing procedures. In …
Detecting Test Tampering Using Item Response Theory
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…
Artificial Intelligence (2 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Mathematics (2 works) · Statistics (2 works) · Academic integrity and plagiarism (1 work) · Advanced Statistical Methods and Models (1 work) · Advanced Statistical Modeling Techniques (1 work) · Arithmetic (1 work) · Econometrics (1 work)