Modelling response error in school effectiveness research

Statistica Neerlandica

Volumn 58, Issue 2, 2004, Pages 138-160

  Fox, Jean Paul ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Classical test theory; Item response theory; MCMC; Random effects model; Response models; School effectiveness; Variance components

Indexed keywords

EID: 1842733394     PISSN: 00390402     EISSN: None     Source Type: Journal    
DOI: 10.1046/j.0039-0402.2003.00253.x     Document Type: Conference Paper

References (39)

1. AITKIN, M. and N. LONGFORD (1986), Statistical modelling in school effectiveness studies, Journal of the Royal Statistical Society, Series A 149, 1-43.
2. ALBERT, J. (1992), Bayesian estimation of normal ogive item response curves using Gibbs sampling, Journal of Educational Statistics 17, 251-269.
3. BAKER, F. B. (1992), Item response theory: parameter estimation techniques, Marcel Dekker, New York.
4. BÉGUIN, A. A. and C. A. W. GLAS (2001), MCMC estimation of multidimensional IRT models, Psychometrika 66, 541-562.
5. BOCK, R. D. and M. AITKIN (1981), Marginal maximum likelihood estimation of item parameters: application of an EM algorithm, Psychometrika 46, 443-459.
6. CONGDON, P. (2002), Bayesian statistical modelling, Wiley, Chichester.
7. DOOLAARD, S. (2002), Stability and change in results of schooling, British Educational Research Journal 28, 773-787.
8. Fox, J.-P. (2001), Multilevel IRT: a Bayesian perspective on estimating parameters and testing statistical hypotheses, Unpublished doctoral dissertation. Twente University, Enschede, Netherlands.
9. Fox, J.-P. and C. A. W. GLAS (2001), Bayesian estimation of a multilevel IRT model using Gibbs sampling, Psychometrika 66, 269-286.
10. Fox, J.-P. and C. A. W. GLAS (2003), Bayesian modeling of measurement error in predictor variables using item response theory, Psychometrika 68, 169-191.
11. GELFAND, A. E., S. E. HILLS, A. RACINE-POON and A. F. M. SMITH (1990), Illustration of Bayesian inference in normal data models using Gibbs sampling, Journal of the American Statistical Association 85, 972-985.
12. GELFAND, A. E. and A. F. M. SMITH (1990), Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association 85, 398-409.
13. GELMAN, A., J. B. CARLIN, H. S. STERN and D. B. RUBIN (1995), Bayesian data analysis, Chapman and Hall, London.
14. GEMAN, S. and D. GEMAN (1984), Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721-741.
15. GILKS, W. R., C. C. WANG, B. YVONNET and P. COURSAGET (1993), Random-effects models for longitudinal data using Gibbs sampling, Biometrics 49, 441-453.
16. GOLDSTEIN, H. (1986), Multilevel mixed linear model analysis using iterative generalized least squares, Biometrika 73, 43-56.
17. GOLDSTEIN, H. (1995), Multilevel statistical models (2nd edn.), Edward Arnold, London.
18. GOLDSTEIN, H. and D. J. SPIEGELHALTER (1996), League tables and their limitations: statistical issues in comparisons of institutional performance, Journal of the Royal Statistical Society, Series A 159, 385-443.
19. JACKSON, P. H. (1973), The estimation of true score variance and error variance in the classical test theory model, Psychometrika 38, 183-201.
20. JENNRICH, R. I. and M. D. SCHLUCHTER (1986), Unbalanced repeated-measures models with structured covariance matrices, Biometrics 42, 805-820.
21. KRISTOF, W. (1969), Estimation of true score and error variance for tests under various equivalence assumptions, Psychometrika 34, 489-507.
22. KRISTOF, W. (1974), Estimation of reliability and true score variance from a split of a test into three arbitrary parts, Psychometrika 39, 491-499.
23. LAIRD, N. M. and J. H. WARE (1982), Random-effects models for longitudinal data, Biometrics 38, 963-974.
24. LINDLEY, D. V. and A. F. M. SMITH (1972), Bayes estimates for the linear model, Journal of the Royal Statistical Society, Series B 34, 1-41.
25. LONGFORD, N. T. (1987), A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects, Biometrika 74, 817-827.
26. LONGFORD, N. T. (1990), Software for variance component analysis of data with nested random effects (maximum likelihood) [Computer program]. Educational Testing Service, Princeton.
27. LONGFORD, N. T. (1993), Random coefficient models, Oxford University Press, New York.
28. LORD, F. M. (1980), Applications of item response theory to practical testing problems, Lawrence Erlbaum Associates, Hillsdale, NJ.
29. LORD, F. M. and M. R. NOVICK (1968), Statistical theories of mental test scores, Addison-Wesley, Reading, MA.
30. METROPOLIS, N., M. N. ROSENBLUTH, A. H. TELLER and E. TELLER (1953), Equations of state calculations by fast computing machines, Journal of Chemical Physics 21, 1087-1092.
31. MOLENAAR, I. W. (1998), Data, model, conclusion, doing it again, Psychometrika 63, 315-340.
32. MUTHÉN, K. L. and B. O. MUTHÉN (1990), Mplus, The comprehensive modeling program for applied researchers [Computer program]. Muthén and Muthén, Los Angeles, CA.
33. PAAP, R. (2002), What are the advantages of MCMC based inference in latent variable models?, Statistica Neerlandica 56, 2-22.
34. PATZ, R. J. and B. W. JUNKER (1999a), A straightforward approach to Markov chain Monte Carlo methods for item response models, Journal of Educational and Behavioral Statistics 24, 146-178.
35. PATZ, R. J. and B. W. JUNKER (1999b), Applications and extensions of MCMC in IRT: multiple item types, missing data, and rated responses, Journal of Educational and Behavioral Statistics 24, 342-366.
36. RAUDENBUSH, S. W., A. S. BRYK, Y. F. CHEONG and R. T. CONGDON JR. (2000), HIM 5. Hierarchical linear and nonlinear modeling [Computer program], Scientific Software International, Lincolnwood, IL.
37. ROBERT, C. P. and G. CASELLA (1999), Monte Carlo statistical methods, Springer, New York.
38. SEARLE, S. R., G. CASELLA and C. E. MCCULLOCH (1992), Variance components, Wiley, New York.
39. VAN DER LINDEN, W. J. and R. K. HAMBLETON (1997), Handbook of modern item response theory, Springer, New York.