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An Empirical Study of the Impact of the Choice of Persistence Models in Value Added Modeling upon Teacher Effect Estimates

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Quantitative Psychology Research

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 140))

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Abstract

It seems that the application of value added modeling (VAM) in educational settings has been gaining momentum in the past decade or so due to the interest in using test scores to evaluate teachers or schools, and currently myriads of VAM models are available for VAM researchers and practitioners. Despite the large number of VAM models, McCaffrey et al. (2004) summarized the relations among them and concluded that many can be viewed as special cases of persistence models. In persistence models, student scores are calculated based on the sum of teacher effects across years. Since different students may change teachers every year and have different membership in multiple group units, such models are also referred to as “multiple membership” models (Browne et al. 2001 Rasbash and Browne 2001). Persistence models differ from each other in the value of the persistence parameter, which, ranging from 0 to 1, denotes how teacher effects at the current year persist into the subsequent years, may it be vanished, undiminished, or diminished. The Variable Persistence (VP) model (Lockwood et al. 2007 McCaffrey et al. 2004) had been considered more flexible due to its free estimation of the persistence parameter, while other persistence models constrain its value to be either 0 or 1.

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References

  • Browne, W. J., Goldstein, H., & Rasbash, J. (2001). Multiple membership multiple classification (MMMC) models. Statistical Modelling: An International Journal, 1, 103–124.

    Article  Google Scholar 

  • Carlin, B., & Louis, T. (2000). Bayes and empirical Bayes methods for data analysis (2nd ed.). Boca Raton, FL: Chapman and Hall/CRC Press.

    Book  MATH  Google Scholar 

  • Diggle, P. J., Liang, K.-Y., & Zeger, S. L. (1996). Analysis of longitudinal data. New York: Oxford University Press.

    Google Scholar 

  • Gelman, A., Carlin, J., Stern, H., & Rubin, D. (1995). Bayesian data analysis. London: Chapman & Hall.

    Google Scholar 

  • Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1996). Markov chain Monte Carlo in practice. London: Chapman & Hall.

    MATH  Google Scholar 

  • Harris, D., & Sass, T. (2006). Value-added models and the measurement of teacher quality. Unpublished manuscript.

    Google Scholar 

  • Karl, A., Yang, Y., & Lohr, S. (2012a). Efficient maximum likelihood estimation for multiple membership mixed models used in value-added modeling. Computational Statistics and Data Analysis, 59, 13–27.

    Article  MathSciNet  Google Scholar 

  • Karl, A. T., Yang, Y., & Lohr, S. (2012b). GPvam: Maximum likelihood estimation of multiple membership mixed models used in value-added modeling. R Package Version 2.0-0. http://cran.r-project.org/web/packages/GPvam/index.html

  • Lockwood, J., McCaffrey, D., Mariano, L., & Setodji, C. (2007). Bayesian methods for scalable multivariate value-added assessment. Journal of Educational and Behavioral Statistics, 32, 125–150.

    Article  Google Scholar 

  • Mariano, L., McCaffrey, D., & Lockwood, J. (2010). A model for teacher effects from longitudinal data without assuming vertical scaling. Journal of Educational and Behavioral Statistics, 35, 253–279.

    Article  Google Scholar 

  • McCaffrey, D., Lockwood, J., Koretz, D., Louis, T., & Hamilton, L. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29, 67–101.

    Article  Google Scholar 

  • Meyer, R. (1997). Value-added indicators of school performance: A primer. Economics of Education Review, 16, 183–301.

    Article  Google Scholar 

  • Rasbash, J., & Browne, W. (2001). Modelling non-hierarchical structures. In A. Leyland & H. Goldstein (Eds.), Multilevel modelling of health statistics (pp. 93–103). West Sussex, England: Wiley.

    Google Scholar 

  • Raudenbusch, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods, second edition. Newbury Park, CA: Sage.

    Google Scholar 

  • Rowan, B., Correnti, R., & Miller, R. J. (2002). What large-scale, survey research tells us about teacher effects on student achievement: Insights from the prospects study of elementary schools. Teachers College Record, 104, 1525–1567.

    Article  Google Scholar 

  • Sanders, W., Saxton, A., & Horn, B. (1997). The Tennessee value-added assessment system: A quantitative outcomes-based approach to educational assessment. In J. Millman (Ed.), Grading teachers, grading schools: Is student achievement a valid evaluational measure? (pp. 137–162). Thousand Oaks, CA: Corwin.

    Google Scholar 

  • Schmidt, W. H., Houang, R. T., & McKnight, C. C. (2005). Value-added research: Right idea but wrong solution? In R. Lissitz (Ed.), Value added models in education: Theory and applications (pp. 145–165). Maple Grove, MN: JAM Press.

    Google Scholar 

  • Shkolnik, J., Hikawa, H., Suttorp, M., Lockwood, J., Stecher, B., & Bohrnstedt, G. (2002). Appendix D: The relationship between teacher characteristics and student achievement in reduced-size classes: A study of 6 California districts. In G. W. Bohrnstedt & B. M. Stecher (Eds.), What we have learned about class size reduction in California Technical Appendix. Palo Alto, CA: American Institutes for Research.

    Google Scholar 

  • Spiegelhalter, D., Thomas, A., Best, N., Gilks, W., & Lunn, D. (2002). BUGS: Bayesian inference using Gibbs sampling. Cambridge, England: MRC Biostatistics Unit. www.mrc-bsu.cam.ac.uk/bugs/.

    Google Scholar 

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Correspondence to Yong Luo .

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Luo, Y., Jiao, H., Lissitz, R. (2015). An Empirical Study of the Impact of the Choice of Persistence Models in Value Added Modeling upon Teacher Effect Estimates. In: van der Ark, L., Bolt, D., Wang, WC., Douglas, J., Chow, SM. (eds) Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-19977-1_11

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