Embedding IRT in structural equation models: A comparison with regression based on IRT scores

Structural Equation Modeling

Volumn 12, Issue 2, 2005, Pages 263-277

  Lu, Irene R R ^a   Thomas, D Roland ^b,d   Zumbo, Bruno D ^c  

^a YORK UNIVERSITY   (Canada)

^b CARLETON UNIVERSITY   (Canada)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^d CARLETON UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRUCTS; DATA SETS; RANDOM EFFECTS; STRUCTURAL EQUATIONS;

COMPUTER SIMULATION; DATA ACQUISITION; ERROR DETECTION; MONTE CARLO METHODS; PARAMETER ESTIMATION; REGRESSION ANALYSIS; SCANNING ELECTRON MICROSCOPY;

PROBLEM SOLVING;

EID: 18444365635     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1207/s15328007sem1202_5     Document Type: Review

References (32)

1. Adams, R. J., Wilson, M., & Wu, M. (1997). Multilevel item response models: An approach to errors in variables regression. Journal of Educational and Behavioral Statistics, 22, 47-76.
2. Arbuckle, J. L., & Wothke, W. (1999). AMOS 4.0 user's guide. Chicago: SPSS, Inc.
3. Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.
4. Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46, 443-459.
5. Bock, R. D., & Lieberman, M. (1970). Fitting a response model for n dichotomously scored items. Psychometrika, 35, 179-197.
6. Christoffersson, A. (1975). Factor analysis of dichotomized variables. Psychometrika, 40, 5-32.
7. Croon, M. (2002). Using predicted latent scores in general latent structure models. In G. A. Marcoulides & I. Moustaki (Eds.), Latent variable and latent structure models (pp. 195-223). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
8. Fuller, W. A. (1987). Measurement error models. New York: Wiley.
9. Hoijtink, H., & Boomsma, A. (1996). Statistical inference based on latent ability estimates. Psychometrika, 61, 313-330.
10. Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8 user's reference guide. Chicago: Scientific Software International.
11. Kim, J. K., & Nicewander, W. A. (1993). Ability estimation for conventional tests. Psychometrika, 58, 587-599.
12. Little, R. J. A., & Rubin, D. B. (1983). On jointly estimating parameters and missing data by maximizing the complete data likelihood, American Statistician, 37, 218-220.
13. Lord, F. M. (1983). Unbiased estimators of ability parameters, of their variance, and of their parallel-forms reliability. Psychometrika, 48, 233-245.
14. Louis, T. A. (1984). Estimating a population of parameter values using Bayes and empirical Bayes methods. Journal of the American Statistical Association, 79(386), 393-398.
15. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
16. Mislevy, R. J. (1987). Exploiting auxiliary information about examinees in the estimation of item parameters. Applied Psychological Measurement, 11, 81-91.
17. Mislevy, R. J., Johnson, E. G., & Muraki, E. (1992). Scaling procedures in NAEP. Journal of Educational Statistics 17, 131-154.
18. Muthén, B. O. (1983). Latent variable structural equation modeling with categorical data. Journal of Econometrics, 22, 43-65.
19. Muthén, B. O. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115-132.
20. Muthén, B. O. (1988). Some uses of structural equation modeling in validity studies: Extending IRT to external variables. In H. Wainer & H. I. Braun (Eds.), Test validity (pp. 213-238). Princeton, NJ: Educational Testing Service.
21. Muthén, B. O., Kao, C. F., & Burstein, L. (1991). Instructionally sensitive psychometrics: Application of a new IRT-based detection technique to mathematics achievement test items. Journal of Educational Measurement, 28, 1-22.
22. Muthén, L. K., & Muthén, B. O. (2001). Mplus user's guide. Los Angeles: Muthén & Muthén.
23. Patz, R. J., & Junker, B. W. (1999). A straightforward approach to Markov Chain Monte Carlo methods for item response models. Journal of Educational and Behavioral Statistics, 24, 146-178.
24. Rubin, D. B. (1987). Multiple imputation for non-response in surveys. New York: Wiley.
25. Schoenberg, R., & Arminger, G. (1990). LINCS2.0. Kent, WA: RJS Software.
26. Shevlin, M., Miles, J. N. V., & Bunting, B. P. (1997). Summated rating scales: A Monte Carlo investigation of the effects of reliability and collinearity in regression models. Personality and Individual Differences, 23, 665-676.
27. Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66, 563-576.
28. Stroud, A. H., & Secrest, D. (1966). Gaussian quadrature formulas. Englewood Cliffs, NJ: Prentice-Hall.
29. Takane, Y., & de Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393-408.
30. Tucker, L. (1971). Relations of factor score estimates to their use. Psychometrika, 36, 427-436.
31. Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427-450.
32. Zwinderman, A. H. (1991). A generalized Rasch model for manifest predictors. Psychometrika, 56, 589-600.