Generalizability in item response modeling

Journal of Educational Measurement

Volumn 44, Issue 2, 2007, Pages 131-155

  Briggs, Derek C ^a   Wilson, Mark ^b  

^a UNIVERSITY OF COLORADO   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34247880236     PISSN: 00220655     EISSN: 17453984     Source Type: Journal    
DOI: 10.1111/j.1745-3984.2007.00031.x     Document Type: Article

References (28)

1. Adams, R. (2006). Reliability as a measurement design effect. Paper presented at the 2006 bi-annual meeting of the Institute for Objective Measurement Workshop, Berkeley, CA.
2. Adams, W. K., Perkins, K. K., Dubson, M., Finkelstein, N. D., & Wieman, C. E. (2005). A new instrument for measuring student beliefs about physics and learning physics: The Colorado learning attitudes about science survey submitted for publication to physical review special topics PER. Retrieved from http://class.colorado.edu/ on May 30, 2006.
3. Betebenner, D. W. (1998). Improved confidence interval estimation for variance components and error variances in generalizability theory. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.
4. Bock, D., Brennan, R., & Muraki, E. (2002). The information in multiple ratings. Applied Psychological Measurement, 26 (4 364 375.
5. Brennan, R. (2001a). Generalizability theory. New York : Springer-Verlag.
6. Brennan, R. (2001b). Manual for urGENOVA. Iowa City, IA : Iowa Testing Programs, University of Iowa.
7. Cornfield, J., & Tukey, J. W. (1956). Average values of mean squares in factorials. Annals of Mathematical Statistics, 27, 907 949.
8. Crick, J. E. & Brennan, R. L. (1983). Manual for GENOVA: A generalized analysis of variance system (American College Testing Technical Bulletin No. 43). Iowa City, IA : ACT, Inc.
9. Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability of scores and profiles. New York : John Wiley.
10. Gao, X., & Brennan, R. L. (2001). Variability of estimated variance components and related statistics in a performance assessment. Applied Measurement in Education, 14, 191 203.
11. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.) Boca Raton, FL: Chapman & Hall/CRC.
12. Goldstein, H. (1995). Multilevel statistical models (2nd ed.). London : Edward Arnold.
13. Green, B., Bock, R., Humphreys, L., Linn, R., & Reckase, M. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21, 347 360.
14. Holland, P. W. (1990). On the sampling theory foundations of item response theory models. Psychometrika, 55(4), 577 601.
15. Kolen, M., & Harris, D. (1987). A multivariate test theory model based on item response theory and generalizability theory. Paper presented at the American Educational Research Association, Washington, DC.
16. Mao, X., Shin, D., & Brennan, R. (April, 2005). Estimating the variability of the estimated variance components and related statistics using the MCMC procedure: An exploratory study. Paper presented at the annual meeting of the National Council on Measurement in Education, Montreal.
17. Mislevy, R. J., Beaton, A. E., Kaplan, B., & Sheehan, K. M. (1992). Estimating population characteristics from sparse matrix samples of item responses. Journal of Educational Measurement, 29, 133 161.
18. Patz, R., & Junker, B. (1999). A straightforward approach to Markov Chain Monte Carlo methods for item response models. Journal of Educational and Behavioral Statistics, 24 (2 146 178.
19. Patz, R. J., Junker, B. W., Johnson, M. S., & Mariano, L. T. (2002). The hierarchical rater model for rated test items and its application to large-scale educational assessment data. Journal of Educational and Behavioral Statistics, 27 (4 341 384.
20. Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.
21. Searle, S. R., Casella, G., & McCulloch, C. E. (1992). Variance components. New York : John Wiley.
22. Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D. (2004). WinBUGS 1.4 (available at http://www.mrc-bsu.cam.ac.uk/bugs).
23. Sturtz, S., Ligges, U. & Gelman, A. (2005). R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software, 12 (3). Available online at http://www.jstatsoft.org/v12/i03/v12i03.pdf.
24. Van den Noortgate, W., & Paek, I. (2004). Person regression models. In P. De Boeck & M. Wilson (Eds. Explanatory item response models (pp. 167 187). New York : Springer.
25. Verhelst, N., & Verstralen, H. (2001). An IRT model for multiple raters. In A. Boomsma, M. van Duijn, & T. Snijders (Eds. Essays on item response theory (pp. 88 108). New York : Springer-Verlag.
26. Wiley, E. W. (2001). Bootstrap strategies for variance component estimation: Theoretical and empirical results. Doctoral dissertation, Stanford University.
27. Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, NJ : Lawrence Erlbaum Associates.
28. Wilson, M., & Hoskens, M. (2001). The rater bundle model. Journal of Educational and Behavioral Statistics, 26 (3 283 306.