Qi Diao
Biographic Data
| ID | 9251762 |
|---|---|
| NAME | Qi Diao |
| GIVEN NAMES | Qi |
| FAMILY NAME | Diao |
| SIGNATURE | DIAO Q |
| AFFILIATIONS | CTB/McGraw-Hill, Monterey, CA, USA |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2014 |
| H-INDEX | 0 |
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 Comparison of Four Item-Selection Methods for Severely Constrained CATs
This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several constraints at the same time). The procedures examined in the study included the weighted deviation model (WDM)…
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 Comparison of Four Item-Selection Methods for Severely Constrained CATs
This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several constraints at the same time). The procedures examined in the study included the weighted deviation model (WDM)…
Mathematics (2 works) · Psychometric Methodologies and Testing (2 works) · Psychometrics (2 works) · Statistics (2 works) · Advanced Statistical Modeling Techniques (1 work) · Algorithm (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Calibration (1 work) · Computer Science (1 work)