Testing the fit of latent regression models

Communications in Statistics - Theory and Methods

Volumn 33, Issue 6, 2004, Pages 1341-1356

  Christensen, Karl Bang ^a,b   Kreiner, Svend ^b  

^a NATIONAL RESEARCH CENTRE FOR THE WORKING ENVIRONMENT   (Denmark)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Goodness of fit; Latent regression models; Power; Rasch models; Sample size

Indexed keywords

COMPUTER SIMULATION; ERROR ANALYSIS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SET THEORY; STATISTICAL TESTS;

GOODNESS OF FIT; LATENT REGRESSION MODELS; POWER; RASH MODELS; SAMPLE SIZE;

REGRESSION ANALYSIS;

EID: 2342628566     PISSN: 03610926     EISSN: None     Source Type: Journal    
DOI: 10.1081/STA-120030153     Document Type: Article

References (18)

1. Adams, R. J., Wilson, M., Wu, M. (1997). Multilevel item response models: An approach to errors in variables regression. J. Educational Behavioral Stat. 22(1):47-76.
2. Christensen, K. B., Bjorner, J. B., Kreiner, S., Petersen, J. H. Latent Regression in Loglinear Rasch Models. Technical Report 03/4; Department of Biostatistics, University of Copenhagen, 2003. Available from http://www.biostat.ku.dk/publ-e.htm.
3. Efron, B., Tibshirani, R. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.
4. Fischer, G. H. (1995). The derivation of polytomous Rasch models. In: Rasch Models - Foundations, Recent Developments, and Applications. Springer-Verlag, pp. 293-307.
5. Fischer, G. H., Molenaar, I. W. (Eds.) (1995). Rasch Models - Foundations, Recent Developments, and Applications. Springer-Verlag.
6. Fox, J. P., Glas, C. A. W. (2001). Bayesian estimation of a multilevel IRT model using Gibbs sampling. Psychometrika 66:271-288.
7. Janssen, R., Tuerlinckx, F., Meulders, M., de Boeck, P. (2000). A hierarchial IRT model for criterion-referenced measurement. J. Educational Behavioral Stat. 25(3):285-306.
8. Kamata, A. (2001). Item analysis by the hierarchial generalized linear model. J. Educational Measurement 38(1):79-93.
9. Kelderman, H. (1984). Loglinear Rasch model tests. Psychometrika 49:223-245.
10. Kelderman, H. (1992). Computing maximum likelihood estimates of loglinear models from marginal sums with special attention to loglinear item response theory. Psychometrika 57:437-450.
11. Maier, K. S. (2001). A Rasch hierarchial measurement model. J. Educational Behavioral Stat. 26(3):307-330.
12. Mislevy, R. J. (1987). Exploiting auxiliary information about examinees in the estimation of item parameters. Appl. Psychol. Measurement 11:81-91.
13. Rao, C. R. (1965). Linear Statistical Inference and its Applications. New York: John Wiley.
14. Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Copenhagen: Danish National Institute for Educational Research (Expanded edition, Chicago: University of Chicago Press; 1980).
15. van der Linden, W. J., Hambleton, R. K. (1997). Handbook of Modern Item Response Theory. Springer-Verlag.
16. Zwinderman, A. H. (1991). A generalized Rasch model for manifest predictors. Psychometrika 56:589-600.
17. Zwinderman, A. H. (1997). Response models with manifest predictors. In: Handbook of Modern Item Response Theory. Springer-Verlag, pp. 245-258.
18. Zwinderman, A. H., van den Wollenberg, A. L. (1990). Robustness of marginal maximum likelihood estimation in the Rasch model. Appl. Psychol. Measurement 14:73-81.