Joint and Conditional Maximum Likelihood Estimation for the Rasch Model for Binary Responses
- Author(s):
- Haberman, Shelby J.
- Publication Year:
- 2004
- Report Number:
- RR-04-20
- Source:
- ETS Research Report
- Document Type:
- Report
- Page Count:
- 63
- Subject/Key Words:
- Rasch Model, Logarithmic Penalty, Entropy, Consistency, Normal Approximation, Marginal Maximum Likelihood Estimation
Abstract
The usefulness of joint and conditional maximum-likelihood is considered for the Rasch model under realistic testing conditions in which the number of examinees is very large and the number is items is relatively large. Conditions for consistency and asymptotic normality are explored, effects of model error are investigated, measures of prediction are estimated, and generalized residuals are developed.
Read More
- Request Copy (specify title and report number, if any)
- http://dx.doi.org/10.1002/j.2333-8504.2004.tb01947.x