Skip to main content
Log in

Reducing Measurement Error in Student Achievement Estimation

  • Theory and Methods
  • Published:
Psychometrika Aims and scope Submit manuscript

Abstract

The achievement level is a variable measured with error, that can be estimated by means of the Rasch model. Teacher grades also measure the achievement level but they are expressed on a different scale. This paper proposes a method for combining these two scores to obtain a synthetic measure of the achievement level based on the theory developed for regression with covariate measurement error. In particular, the focus is on ordinal scaled grades, using the SIMEX method for measurement error correction. The result is a measure comparable across subjects with smaller measurement error variance. An empirical application illustrates the method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aitkin, M., & Rocci, R. (2002). A general maximum likelihood analysis of measurement error in generalized linear models. Statistics and Computing, 12, 163–74.

    Article  Google Scholar 

  • Barndorff-Nielsen, O.E., & Cox, D.R. (1994). Inference and asymptotics. London: Chapman and Hall.

    Google Scholar 

  • Bartholomew, D.J., & Knott, M. (1999). Latent variable models and factor analysis. London: Edward Arnold.

    Google Scholar 

  • Birnbaum, A. (1968). Test scores, sufficient statistics, and the information structures of tests. In F.M. Lord & M.R. Novick (Eds.), Statistical theories of mental test scores. Reading: Addison-Wesley.

    Google Scholar 

  • Greene, W.F., & Cai, J. (2004). Measurement error in covariates in the marginal hazards model for multivariate failure time data. Biometrics, 60, 987–96.

    Article  PubMed  Google Scholar 

  • Carroll, R.J., Küchenhoff, H., Lombard, F., & Stefanski, L.A. (1996). Asymptotics for the SIMEX estimator in nonlinear measurement error models. Journal of the American Statistical Association, 91, 242–50.

    Article  Google Scholar 

  • Carroll, R.J., Ruppert, D., Stefanski, L.A., & Crainiceanu, C. (2006). Measurement error in nonlinear models (2nd ed.). London: Chapman and Hall.

    Google Scholar 

  • Casella, G., & Berger, R.L. (2002). Statistical inference (2nd ed.). Pacific Grove: Duxbury Press.

    Google Scholar 

  • Chesher, A. (1991). The effect of measurement error. Biometrika, 78, 451–62.

    Article  Google Scholar 

  • Cook, J., & Stefanski, L.A. (1994). A simulation extrapolation method for parametric measurement error models. Journal of the American Statistical Association, 89, 1314–328.

    Article  Google Scholar 

  • Fuller, W.A. (1987). Measurement error models. New York: Wiley.

    Book  Google Scholar 

  • Goldstein, H., & Thomas, S. (1996). Using examination results as indicators of school and college performance. Journal of the Royal Statistical Society Series A, 159, 149–63.

    Google Scholar 

  • Good, F. (1988). A method of moderation of school-based assessments: some statistical considerations. The Statistician, 37, 33–9.

    Article  Google Scholar 

  • Hoijtink, H., & Boomsma, A. (1995). On person parameter estimation in the dichotomous Rasch model. In G.H. Fischer & I.W. Molenaar (Eds.), Rasch models: Foundations, recent developments and applications. New York: Springer.

    Google Scholar 

  • Levin-Koh, S.C., & Amemiya, Y. (2003). Heteroscedastic factor analysis. Biometrika, 90, 85–7.

    Article  Google Scholar 

  • Molenaar, I.W. (1995). Some background for item response theory and the Rasch model. In G.H. Fischer & I.W. Molenaar (Eds.), Rasch models: Foundations, recent developments and applications. New York: Springer.

    Google Scholar 

  • Muthén, B.O. (1984). A general structural equation model with dichotomous, ordered categorical and continuous latent indicators. Psychometrika, 49, 115–32.

    Article  Google Scholar 

  • Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.

    Google Scholar 

  • Shi, J.Q., & Lee, S.Y. (2000). Latent variable models with mixed continuous and polytomous data. Journal of the Royal Statistical Society Series B, 62, 77–7.

    Article  Google Scholar 

  • Snijders, T., & Bosker, R. (1999). An introduction to basic and advanced multilevel modeling. London: Sage.

    Google Scholar 

  • Stefanski, L.A., & Cook, J.R. (1995). Simulation-extrapolation: The measurement error jackknife. Journal of the American Statistical Association, 90, 1247–256.

    Article  Google Scholar 

  • Tekwe, C.D., Carter, R.L., Ma, C.X., Algina, J., Lucas, M.E., Roth, J., Ariet, M., Fisher, T., & Resnick, M.B. (2004). An empirical comparison of statistical models for value-added assessment of school performance. Journal of Educational and Behavioral Statistics, 29, 11–6.

    Article  Google Scholar 

  • Wang, N., Lin, X., Gutierrez, R.G., & Carroll, R.J. (1998). Bias analysis and SIMEX approach in generalized linear mixed measurement error models. Journal of the American Statistical Association, 93, 249–61.

    Article  Google Scholar 

  • Woodhouse, G., Yang, M., Goldstein, H., & Rasbash, J. (1996). Adjusting for measurement error in multilevel analysis. Journal of the Royal Statistical Society Series A, 159, 201–12.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michela Battauz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Battauz, M., Bellio, R. & Gori, E. Reducing Measurement Error in Student Achievement Estimation. Psychometrika 73, 289–302 (2008). https://doi.org/10.1007/s11336-007-9050-z

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11336-007-9050-z

Keywords

Navigation