A mixture rasch model with item response time components

Applied Psychological Measurement

Volumn 34, Issue 7, 2010, Pages 521-538

  Meyer, J Patrick ^a  

^a UNIVERSITY OF VIRGINIA   (United States)

Author keywords

Bayesian estimation; mixture IRT models; response time; simulation study

Indexed keywords

EID: 77956303098     PISSN: 01466216     EISSN: 15523497     Source Type: Journal    
DOI: 10.1177/0146621609355451     Document Type: Article

References (33)

1. Bolt, D.M., Cohen, A.S., & Wollack, J.A. (2002). Item parameter estimation under conditions of test speededness: Application of a mixture Rasch model with ordinal constraints. Journal of Educational Measurement, 39, 331-348.
2. Boughton, K.A., & Yamamoto, K. (2007). A hybrid model for test speededness. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models (pp. 147-156). New York: Springer.
3. Brooks, S.P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455.
4. Cameron, L., Wise, S.L., & Lottridge, S.M. (2007). The development and validation of the information literacy test. College and Research Libraries, 68, 229-236.
5. Cho, S.-J., Cohen, A.S., & Kim, S.-H. (2006, February). Modeling prior information for detection of latent classes in the mixture Rasch model. Paper presented at the annual meeting of the Psychometric Society, Montreal.
6. Congdon, P. (2003). Applied Bayesian modeling. West Sussex, UK: John R. Wiley & Sons.
7. Douglas, J., Kim, H.R., Habing, B., & Gao, F. (1998). Investigating local dependence with conditional covariance functions. Journal of Educational and Behavioral Statistics, 23, 129-151.
8. Fraley, C., & Raftery, A. (2007). The mclust package. Retrieved fromhttp://www.stat.washington.edu/mclust/.
9. Gelfand, A.E., & Ghosh, S.K. (1998). Model choice: A minimum posterior predictive loss approach. Biometrika, 85, 1-11.
10. Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (Eds.). (1996). Markov chain Monte Carlo in practice. London: Chapman & Hall/ CRC.
11. Laud, P., & Ibrahim, J. (1995). Predictive model selection. Journal of the Royal Statistical Society, B 57, 247-262.
12. Li, F., Cohen, A.S., Kim, S.-H., & Cho, S.-J. (2009). Model selection methods for mixture dichotomous IRT models. Applied Psychological Measurement, 33, 353-373.
13. Linacre, J.M. (2008). A user's guide to WINSTEPS, MINISTEP Rasch-model computer programs. Chicago: John M. Linacre.
14. McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: John R. Wiley & Sons.
15. Mouchart, M., & San Martín, E. (2003). Specification and identification issues in models involving a latent hierarchical structure. Journal of Statistical Planning and Inference, 111, 143-163.
16. Mroch, A.A., Bolt, D.M., & Wollack, J.A. (2005, April). A new multi-class mixture Rasch model for test speededness. Paper presented at the annual meeting of the National Council on Measurement in Education, Montreal, Quebec.
17. Oshima, T.C. (1994). The effect of speededness on parameter estimation in item response theory. Journal of Educational Measurement, 31, 200-219.
18. Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271-282.
19. Schnipke, D.L., & Scrams, D.J. (1997). Modeling item response times with a two-state mixture model: A new method of measuring speededness. Journal of Educational Measurement, 34, 213-232.
20. Schnipke, D.L., & Scrams, D.J. (2002). Exploring issues of examinee behavior: Insights gained from response time analyses. In C. N. Mills, M. Potenza, J. J. Fremer, & W. Ward (Eds.), Computer-based testing: Building the foundation for future assessments (pp. 237-266). Mahwah, NJ: Lawrence Erlbaum.
21. Silverman, B.W. (1986). Density estimation for statistics and data analysis. New York: Chapman & Hall/ CRC.
22. Sinharay, S. (2004). Experiences with Markov chain Monte Carlo convergence assessment. Journal of Educational and Behavioral Statistics, 29, 461-488.
23. Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D. (2007). Openbugs: User manual, version 3.0.2. Retrieved fromhttp://mathstat.helsinki.fi/openbugs/.
24. Thissen, D. (1983). Timed testing: An approach using item response theory. In D. J. Weiss (Ed.), New horizons in testing: Latent trait theory and computerized adaptive testing (pp. 179-203). New York: Academic Press.
25. van der Linden, W.J. (2006). A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics, 31, 181-204.
26. van der Linden, W.J. (2007). A hierarchical framework for modeling speed and accuracy on test items. Psychometrika, 72, 287-308.
27. van der Linden, W.J., Scrams, D.J., & Schnipke, D.L. (1999). Using response-time constraints to control for differential speededness in computerized adaptive testing. Applied Psychological Measurement, 23, 195-210.
28. van der Linden, W.J., & van Krimpen-Stoop, E.M.L.A. (2003). Using response times to detect aberrant responses in computerized adaptive testing. Psychometrika, 68, 251-265.
29. von Davier, M., & Rost, J. (1995). Polytomous mixed Rasch models. In G. H. Fisher & I. W. Molenaar (Eds.), Rasch models: Foundations, recent developments, and applications (pp. 371-379). New York: Springer.
30. Wise, S.L., & DeMars, C.E. (2006). An application of item response time: The effort moderated IRT model. Journal of Educational Measurement, 43, 19-38.
31. Wise, S.L., & Kong, X.J. (2005). Response time effort: A new measure of examinee motivation in computer-based tests. Applied Measurement in Education, 16, 163-183.
32. Wollack, J.A., Cohen, A.S., & Wells, C.S. (2003). A method for maintaining scale stability in the presence of test speededness. Journal of Educational Measurement, 40, 307-330.
33. Yamamoto, K., & Everson, H. (1997). Modeling the effects of test length and test time on parameter estimation using the HYBRID model. In J. Rost (Ed.), Applications of latent trait and latent class models in the social sciences (pp. 89-98). Münster, Germany: Waxmann.