A Rasch model for detecting learning while solving an intelligence test

Applied Psychological Measurement

Volumn 24, Issue 2, 2000, Pages 151-162

  Verguts, Tom ^a   De Boeck, Paul ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Bayesian statistics; Dynamic Rasch model; Intelligence tests; Learning; Rasch model

Indexed keywords

EID: 0034335660     PISSN: 01466216     EISSN: None     Source Type: Journal    
DOI: 10.1177/01466210022031589     Document Type: Article

References (36)

1. Albert, J. H. (1992). Bayesian estimation of normal ogive item response curves using Gibbs sampling. Journal of Educational Statistics, 17, 251-269.
2. Albert, J. H., &Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669-679.
3. Baker, F. B. (1998). An investigation of the item parameter recovery characteristics of a Gibbs sampling procedure. Applied Psychological Measurement, 22, 153-169.
4. Billman, D., & Knutson, J. (1996). Unsupervised concept learning and value systematicity: A complex whole aids learning the parts. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 458-475.
5. Box, G. E. P., & Tiao, G. C. (1973). Bayesian inference in statistical analysis. New York: Wiley.
6. Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of processing in the Raven Progressive Matrices Test. Psychological Review, 97, 404-431.
7. Casella, G., & George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46, 19-26.
8. Embretson, S. E. (1980). Multicomponent latent trait models for ability tests. Psychometrika, 45, 479-494.
9. Embretson, S. E. (1991). A multidimensional latent trait model for measuring learning and change. Psychometrika, 56, 495-515.
10. Fischer, G. H. (1973). The linear logistic test model as an instrument in educational research. Acta Psychologica, 37, 359-374.
11. Fischer, G. H. (1983). Logistic latent trait models with linear constraints. Psychometrika, 48, 3-26.
12. Fischer, G. H. (1995). Some neglected problems in IRT. Psychometrika, 60, 459-487.
13. Fischer, G. H., & Formann, A. K. (1982). Some applications of logistic latent trait models with linear constraints on the parameters. Applied Psychological Measurement, 6, 397-416.
14. Gelfand, A. E., & Smith, A. F. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409.
15. Gelman, A. (1996). Inference and monitoring convergence. In W. Gilks, S. Richardson, & D. Spiegelhalter (Eds.), Markov chain monte carlo in practice (pp. 131-143). New York: Chapman and Hall.
16. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. New York: Chapman and Hall.
17. Gelman, A., & Meng, X. L. (1996). Model checking and model improvement. In W. Gilks, S. Richardson, & D. Spiegelhalter (Eds.), Markov chain monte carlo in practice (pp. 189-201). New York: Chapman and Hall.
18. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
19. Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Theory and applications. Boston MA: Kluwer-Nijhoff.
20. Holland, P. W. (1990). On the sampling theory foundations of item response theory models. Psychometrika, 55, 577-601.
21. Hoskens, M., & De Boeck, P. (1997). A parametric model for local dependence among test items. Psychological Methods, 2, 261-277.
22. Janssen, R., Tuerlinckx, F., Meulders, M., & De Boeck, P. (in press). An hierarchical model for criterion-referenced measurement. Journal of Educational and Behavioral Statistics.
23. Kelderman, H. (1984). Loglinear Rasch model tests. Psychometrika, 49, 223-245.
24. Kempf, W. F. (1977). Dynamic models for the measurement of traits in social behavior. In W. F. Kempf, E. B. Andersen, & B. H. Repp (Eds.), Mathematical models for social psychology (pp. 14-58). Bern: Huber.
25. Kersten, A. W., & Billman, D. (1997). Event category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 638-658.
26. Maris, E. (1995). Psychometric latent response models. Psychometrika, 60, 523-547.
27. McKinley, R. L., & Reckase, M. D. (1983). MAXLOG: A computer program for the estimation of the parameters of a multidimensional logistic model. Behavior Research Methods and Instrumentation, 15, 389-390.
28. Mislevy, R. J., & Bock, R. D. (1989). PC-BILOG: Item analysis and test scoring with binary logistic models [Computer program]. Mooresville IN: Scientific Software.
29. Scheiblechner, H. (1972). Das Lernen und Lösen komplexer Denkaufgaben. Zeitschrift für Experimentelle und Angewandte Psychologie, 19, 476-506.
30. Schervisch, M. J. (1995). Theory of statistics. New York: Springer-Verlag.
31. Spada, H. (1976). Modelle des Denkens and Lernens. Bern: Huber.
32. Swaminathan, H. & Gifford, J.A. (1982). Bayesian estimation in the Rasch model. Journal of Educational Statistics, 7, 175-191.
33. Tanner, M. A. (1996). Tools for statistical inference. New York: Springer.
34. Tsutakawa, R. K. (1984). Estimation of two-parameter logistic item response curves. Journal of Educational Statistics, 9, 263-276.
35. Verhelst, N. D., & Glas, C. A. W. (1993). A dynamic generalization of the Rasch model. Psychometrika, 58, 395-415.
36. Verhelst, N. D., & Glas, C. A. W. (1995). Dynamic generalizations of the Rasch model. In G. H. Fischer, & I. W. Molenaar (Eds.), Rasch models: Foundations, recent developments, and applications (pp. 181-201). New York: Springer-Verlag.