A -Stratified Computerized Adaptive Testing in the Presence of Calibration Error
Bibliographic Data
| ID | 20287665 |
|---|---|
| Authors | Ying Cheng (0000-0001-7732-9806, University of Notre Dame), Jeffrey M Patton (University of Notre Dame, Notre Dame, IN, USA), Can Shao (0000-0002-4528-6714, University of Notre Dame, Notre Dame, IN, USA) |
| Year | 2015 |
| Volume | 75 |
| Issue | 2 |
| Pages | 260-283 |
| Publication date | 2015-04-01 |
| 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 | 10.1177/0013164414530719 |
| PMID | 29795821 |
| OpenAlex | W2135625571 |
| Language | EN |
| Citations received | 4 |
| References cited | 23 |
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, capitalization on chance may occur. In this article, we examined the performance of the AST method under more realistic conditions where item parameter estimates instead of true parameter values are used in the CAT. Its performance was compared against that of the MFI method when the latter is used in conjunction with Sympson–Hetter or randomesque exposure control. Results indicate that the MFI method, even when combined with exposure control, is susceptible to capitalization on chance. This is particularly true when the calibration sample size is small. On the other hand, AST is more robust to capitalization on chance. Consistent with previous investigations using true item parameter values, AST yields much more balanced item pool usage, with a small loss in the precision of latent trait estimates. The loss is negligible when the test is as long as 40 items
Calibration · Computerized adaptive testing · Econometrics · Item response theory · Population · Psychometrics · Sample size determination · Statistics · Advanced Statistical Modeling Techniques · Computer Science · Grit, Self-Efficacy, and Motivation · Mathematics · Psychometric Methodologies and Testing
| Citations per year | 0.44 |
|---|---|
| Citation span | 2017 - 2026 (10) |
| Highly cited | No |