A comparison of approaches for improving the reliability of objective level scores

Educational and Psychological Measurement

Volumn 70, Issue 3, 2010, Pages 357-375

  Skorupski, William P ^a   Carvajal, Jorge ^a  

^a UNIVERSITY OF KANSAS   (United States)

Author keywords

Objective level scores; Reliability; Subscore augmentation

Indexed keywords

EID: 77953988559     PISSN: 00131644     EISSN: 15523888     Source Type: Journal    
DOI: 10.1177/0013164409355694     Document Type: Article

References (17)

1. American Educational Research Association, American Psychological Association, National Council on Measurement in Education. (1999). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.
2. Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. New York CBS College.
3. Edwards, M.C., & Vevea, J.L. (2006). An empirical Bayes approach to subscore augmentation: How much strength can we borrow? Journal of Educational and Behavioral Statistics, 31, 241-259.
4. Goodman, D.P., & Hambleton, R.K. (2004). Student test score reports and interpretive guides: Review of current practices and suggestions for future research. Applied Measurement in Education, 17, 145-220.
5. Green, D.R., Yen, W.M., & Burket, G.R. (1989). Experiences in the application of item response theory in test construction. Applied Measurement in Education, 2, 297-312.
6. Hambleton, R.K., & Swaminathan, H. (1985). Item response theory principles and applications. Boston: Kluwer-Nijhoff.
7. Kelley, T.L. (1927). The interpretation of educational measurements. New York: World Book.
8. Kelley, T.L. (1947). Fundamentals of statistics. Cambridge, MA: Harvard University Press.
9. Lord, F.M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Lawrence Erlbaum.
10. Luecht, R.M. (2003, April). Applications of multidimensional diagnostic scoring for certification and licensure tests. Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago, IL.
11. Messick, S. (1989). Validity. In R. Linn (Ed.), Educational measurement (3rd ed., pp. 13-103). Washington, DC: American Council on Education.
12. Skorupski, W.P. (2008). SUBSKOR [Computer Software]. Lawrence: University of Kansas.
13. U.S. Department of Education. (2002). No Child Left Behind Act of 2001 (Pub. L. No. 107-110, §1111, 115 Stat. 1449-1452) (2002). Washington, DC: Author.
14. Wainer, H., Sheehan, K.M., & Wang, X. (2000). Some paths towards making Praxis scores more useful. Journal of Educational Measurement, 37, 113-140.
15. Wainer, H., Vevea, J.L., Camacho, F., Reeve, B.B., Rosa, K., Nelson, L.,et al.(2001). Augmented score-''borrowing strength'' to compute scores based on small numbers of items. In D. Thissen & H. Wainer (Eds.), Test scoring (pp. 343-387). Mahwah, NJ: Lawrence Erlbaum.
16. Yen, W.M. (1987, June). A Bayesian/IRT index of objective performance. Paper presented at the meeting of the Psychometric Society, Montreal, CA.
17. Zimowski, M.F., Muraki, E., Mislevy, R.J., & Bock, R.D. (2002). BILOG-MG[Computer software]. Chicago: Scientific Software International.