| Title: |
Investigating Confidence Intervals of Item Parameters When Some Item Parameters Take Priors in the 2PL and 3PL Models |
| Language: |
English |
| Authors: |
Paek, Insu (ORCID 0000-0002-2552-9475); Lin, Zhongtian; Chalmers, Robert Philip |
| Source: |
Educational and Psychological Measurement. Apr 2023 83(2):375-400. |
| Availability: |
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: |
Y |
| Page Count: |
26 |
| Publication Date: |
2023 |
| Document Type: |
Journal Articles; Reports - Research |
| Descriptors: |
Models; Item Response Theory; Test Items; Intervals; Simulation; Statistical Distributions; Error of Measurement; Test Length; Sample Size; Statistical Bias |
| DOI: |
10.1177/00131644221096431 |
| ISSN: |
0013-1644; 1552-3888 |
| Abstract: |
To reduce the chance of Heywood cases or nonconvergence in estimating the 2PL or the 3PL model in the marginal maximum likelihood with the expectation-maximization (MML-EM) estimation method, priors for the item slope parameter in the 2PL model or for the pseudo-guessing parameter in the 3PL model can be used and the marginal maximum a posteriori (MMAP) and posterior standard error (PSE) are estimated. Confidence intervals (CIs) for these parameters and other parameters which did not take any priors were investigated with popular prior distributions, different error covariance estimation methods, test lengths, and sample sizes. A seemingly paradoxical result was that, when priors were taken, the conditions of the error covariance estimation methods known to be better in the literature (Louis or Oakes method in this study) did not yield the best results for the CI performance, while the conditions of the cross-product method for the error covariance estimation which has the tendency of upward bias in estimating the standard errors exhibited better CI performance. Other important findings for the CI performance are also discussed. |
| Abstractor: |
As Provided |
| Entry Date: |
2023 |
| Accession Number: |
EJ1367466 |
| Database: |
ERIC |