Jeffrey M Patton
Biographic Data
| ID | 9254145 |
|---|---|
| NAME | Jeffrey M Patton |
| GIVEN NAMES | Jeffrey M |
| FAMILY NAME | Patton |
| SIGNATURE | PATTON J M |
| AFFILIATIONS | University of Notre Dame, Notre Dame, IN, USA |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2015 |
| H-INDEX | 0 |
A -Stratified Computerized Adaptive Testing in the Presence of Calibration Error
a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated item parameter estimates as if they are the true population parameter values. Consequently, capitalizat…
Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown
When item parameter estimates are used to estimate the ability parameter in item response models, the standard error ( SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the covariance matrix of item parameter estimates, which may not be available. An alternative SE can be obtai…
No prominent works on this page.
Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown
When item parameter estimates are used to estimate the ability parameter in item response models, the standard error ( SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the covariance matrix of item parameter estimates, which may not be available. An alternative SE can be obtai…
A -Stratified Computerized Adaptive Testing in the Presence of Calibration Error
a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated item parameter estimates as if they are the true population parameter values. Consequently, capitalizat…
Advanced Statistical Modeling Techniques (2 works) · Calibration (2 works) · Econometrics (2 works) · Item response theory (2 works) · Mathematics (2 works) · Psychometric Methodologies and Testing (2 works) · Psychometrics (2 works) · Statistics (2 works) · Computer Science (1 work) · Computerized adaptive testing (1 work)