Submitted by Cliff Donath on Sun, 07/10/2011 - 07:30
| Title | Data sparseness and on-line pretest item calibration-scaling methods in CAT |
| Publication Type | Journal Article |
| Year of Publication | 2002 |
| Authors | Ban, J-C, Hanson, BA, Yi, Q, Harris, DJ |
| Journal | Journal of Educational Measurement |
| Volume | 39 |
| Number | 3 |
| Pagination | 207-218 |
| Publication Language | eng |
| Keywords | Computer Assisted Testing, Educational Measurement, Item Response Theory, Maximum Likelihood, Methodology, Scaling (Testing), Statistical Data |
| Abstract | Compared and evaluated 3 on-line pretest item calibration-scaling methods (the marginal maximum likelihood estimate with 1 expectation maximization [EM] cycle [OEM] method, the marginal maximum likelihood estimate with multiple EM cycles [MEM] method, and M. L. Stocking's Method B) in terms of item parameter recovery when the item responses to the pretest items in the pool are sparse. Simulations of computerized adaptive tests were used to evaluate the results yielded by the three methods. The MEM method produced the smallest average total error in parameter estimation, and the OEM method yielded the largest total error (PsycINFO Database Record (c) 2005 APA ) |