The impact of fallible item parameter estimates on latent trait recovery

Psychometrika

Volumn 75, Issue 2, 2010, Pages 280-291

  Cheng, Ying ^a   Yuan, Ke Hai ^a  

^a UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

IRT scoring; Latent trait estimation; Pseudo maximum likelihood (PML); Standard error; Upward correction

Indexed keywords

EID: 77953728233     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11336-009-9144-x     Document Type: Article

References (17)

1. Baker, F. B., & Kim, S. H. (2004). Item response theory: Parameter estimation techniques (2nd ed.). New York: Dekker.
2. Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores (pp. 397-472). Reading: Addison-Wesley.
3. Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46, 443-459.
4. Cai, L. (2008). SEM of another flavour: Two new applications of the supplemented EM algorithm. British Journal of Mathematical and Statistical Psychology, 61, 309-329.
5. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley.
6. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah: Erlbaum.
7. Godambe, V. P., & Thompson, M. E. (1974). Estimating equations in the presence of a nuisance parameter. The Annals of Statistics, 2, 568-571.
8. Gong, G., & Samaniego, F. (1981). Pseudo-maximum likelihood estimation: Theory and applications. The Annals of Statistics, 9, 861-869.
9. Hambleton, R., & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston: Kluwer-Nijhoff Publishing.
10. Harwell, M. R., & Baker, F. B. (1991). The use of prior distributions in marginalized Bayesian item parameter estimation: A didactic. Applied Psychological Measurement, 15, 375-389.
11. Harwell, M. R., Baker, F. B., & Zwarts, M. (1988). Item parameter estimation via marginal maximum likelihood and an EM algorithm: A didactic. Journal of Educational Statistics, 13, 243-271.
12. Parke, W. R. (1986). Pseudo maximum likelihood estimation: The asymptotic distribution. The Annals of Statistics, 14, 355-357.
13. Patz, R. J., & Junker, B. W. (1999). A straightforward approach to Markov chain Monte Carlo methods for item response models. Journal of Educational and Behavioral Statistics, 24, 146-178.
14. Tsutakawa, R. K., & Johnson, J. C. (1990). The effect of uncertainty of item parameter estimation on ability estimates. Psychometrika, 55, 371-390.
15. Yuan, K.-H., & Jennrich, R. I. (2000). Estimating equations with nuisance parameters: Theory and applications. Annals of the Institute of Statistical Mathematics, 52, 343-350.
16. Wingersky, M. S., Barton, M. A., & Lord, F. M. (1982). LOGIST user's guide. Princeton: Educational Testing Service.
17. Zimowski, M., Muraki, E., Mislevy, R. J., & Bock, R. D. (2003). BILOG-MG 3: Item analysis and test scoring with binary logistic models. Chicago: Scientific Software [Computer software].