Approximations of Normal IRT Models for Change

Journal of Educational and Behavioral Statistics

Volumn 24, Issue 2, 1999, Pages 208-223

  Tan, Frans E S ^a   Ambergen, Anton W ^a   Does, Ronald J M M ^a   Imbos, Tjaart ^a  

^a NONE

Author keywords

Closed form estimators; IRT; Longitudinal

Indexed keywords

EID: 0347655100     PISSN: 10769986     EISSN: None     Source Type: Journal    
DOI: 10.3102/10769986024002208     Document Type: Article

References (19)

1. Adams, R. J., & Wilson, M (1996). Formulating the Rasch model as a mixed coefficients multinomial logit, chapter 9. In G. Engelhard & M. Wilson (Eds.), Objective Measurement: Theory and Practice, volume 3. New Jersey: Ablex.
2. Albers W., Does, R. J. M. M., Imbos, Tj., & Janssen, M. P E. (1989) A stochastic growth model applied to repeated tests of academic knowledge. Psychometrika, 54, 451-466.
3. Andersen E. B. (1980). Comparing latent distributions. Psychometrika, 45, 121-134.
4. Billingsley P. (1979). Probability and Measure. New York: Wiley.
5. Diggle, P. J., Liang, K. Y., & Zeger S. L. (1994). Analysis of longitudinal data. Oxford: Clarendon Press.
6. Embretson, S. E. (1991). A multidimensional latent trait model for measuring learning change. Psychometrika, 56, 495-516.
7. Embretson, S. E. (1997). Structured ability models in tests designed from cognitive theory. In M. Wilson, G. Engelhard, & K. Draney (Eds.), Objective measurement IV: Theory into practice. Greenwich, CT: Ablex Publishing Corp.
8. Hoijtink (1995). Linear and repeated measures models for the person parameters. In G. H. Fisher, & I. W. Molenaar, (Eds.), Rasch models, foundations, recent developments, and applications. New York: Springer-Verlag.
9. Lord, F. M. (1980). Applications of item response theory to practical testing problems. New Jersey: Lawrence Erlbaum.
10. Rao, C. R. (1973). Linear statistical inference and its applications. New York: Wiley.
11. Rogossa, D., & Saner, H. (1995). Longitudinal data analysis examples with random coefficient. Journal of educational and behavioral statistics, 20, 149-171.
12. Schmidt, H. G. (1983). Problem-based learning: rationale and description. Medical Education, 17, 11-16.
13. Tan, E. S. (1994). A stochastic growth model for the longitudinal measurement of ability. Ph.D. thesis, University of Amsterdam.
14. Tan, E. S., Ambergen, A. W., Willems, N. H. A., Does R. J. M. M., & Imbos Tj. (1996). Approximations of the normal ogive and large sample efficiency of closed form parameter estimator. Technical report Methodology and Statistics no. 1. University of Maastricht.
15. Verhelst, N. D., & Glas, C. A. W. (1995a). Dynamic generalizations of the Rasch model chapter 10. In G. H. Fisher & I. W. Molenaar (Eds.), Rasch models, foundations, recent developments, and applications. New York: Springer-Verlag.
16. Verhelst, N. D., & Glas, C. A. W. (1995b). The one parameter logistic model chapter 12. In G. H. Fisher & I. W. Molenaar, Rasch models, foundations, recent developments, and applications. New York: Springer-Verlag
17. Vonesh, E. F., & Carter, R. L. (1987). Efficient inference for a random coefficient growth curve model with unbalanced data. Biometrics, 43, 617-628.
18. Vonesh, E. F. (1992). Non-linear models for the analysis of longitudinal data. Statistics in Medicine 11, 1929-1955
19. Wang, W., & Wilson, M. (1997). Between-item and within-item multidimensionality. In M. Wilson, G. Engelhard, & K. Draney (Eds.), Objective measurement IV: Theory in practice. Greenwich, CT: Ablex Publishing Corp.