IDEAS home Printed from https://ideas.repec.org/p/fth/hertbu/20007.html
   My bibliography  Save this paper

A Comparison of Modelling Strategies for Value-Added Analyses of Educational Data

Author

Listed:
  • Spencer, N.
  • Fielding, A.

Abstract

Modelling strategies for value-added multilevel models are examined. These types of models typically include an endogenous variable and this causes difficulties for the standard estimation techniques that are commonly used to analyse multilevel models. Two alternative estimation strategies are proposed: one using an instrumental variable approach and the other using a Bayesian analysis through the BUGS software. We conclud that the approach offered by the BUGS software has advantages over more classical estimation methods.

Suggested Citation

  • Spencer, N. & Fielding, A., 2000. "A Comparison of Modelling Strategies for Value-Added Analyses of Educational Data," Papers 2000:7, University of Hertfordshire - Business Schoool.
    • Handle: RePEc:fth:hertbu:2000:7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    2. Harvey Goldstein & Sally Thomas, 1996. "Using Examination Results as Indicators of School and College Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(1), pages 149-163, January.
    3. Spencer, N.H., 1998. "Consistent Parameter Estimation for Lagged Multilevel Models," Papers 1998:19, University of Hertfordshire - Business Schoool.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peter Ebbes & Ulf Böckenholt & Michel Wedel, 2004. "Regressor and random‐effects dependencies in multilevel models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 161-178, May.
    2. Neil Spencer, 2002. "Combining Modelling Strategies to Analyse Teaching Styles Data," Quality & Quantity: International Journal of Methodology, Springer, vol. 36(2), pages 113-127, May.
    3. Jorge Manzi & Ernesto San Martín & Sébastien Van Bellegem, 2014. "School System Evaluation by Value Added Analysis Under Endogeneity," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 130-153, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    2. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    3. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    4. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    5. Pickery, Jan, 2002. "Contextual effects on the vote in Germany: A multilevel analysis," Discussion Papers, Research Unit: Institutions and Social Change FS III 02-202, WZB Berlin Social Science Center.
    6. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Other publications TiSEM f5784a49-7053-401d-855d-1, Tilburg University, School of Economics and Management.
    7. Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
    8. Manzi, Jorge & San Martin, Ernesto & Van Bellegem, Sébastien, 2010. "School System Evaluation By Value-Added Analysis under Endogeneity," IDEI Working Papers 631, Institut d'Économie Industrielle (IDEI), Toulouse.
    9. Ralf van der Lans & Bram Van den Bergh & Evelien Dieleman, 2014. "Partner Selection in Brand Alliances: An Empirical Investigation of the Drivers of Brand Fit," Marketing Science, INFORMS, vol. 33(4), pages 551-566, July.
    10. Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.
    11. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    12. Terence D.Agbeyegbe & Elena Goldman, 2005. "Estimation of threshold time series models using efficient jump MCMC," Economics Working Paper Archive at Hunter College 406, Hunter College Department of Economics, revised 2005.
    13. Ockerman, Daniel H. & Goldsman, David, 1999. "Student t-tests and compound tests to detect transients in simulated time series," European Journal of Operational Research, Elsevier, vol. 116(3), pages 681-691, August.
    14. Adnett, Nick & Bougheas, Spiros & Davies, Peter, 2002. "Market-based reforms of public schooling: some unpleasant dynamics," Economics of Education Review, Elsevier, vol. 21(4), pages 323-330, August.
    15. Hong, Yi & Jin, Xing, 2022. "Pricing of variance swap rates and investment decisions of variance swaps: Evidence from a three-factor model," European Journal of Operational Research, Elsevier, vol. 303(2), pages 975-985.
    16. Shofiqul Islam & Sonia Anand & Jemila Hamid & Lehana Thabane & Joseph Beyene, 2020. "A copula-based method of classifying individuals into binary disease categories using dependent biomarkers," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 871-897, December.
    17. da-Silva, C.Q. & Gomes, A.E., 2011. "Bayesian inference for an item response model for modeling test anxiety," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3165-3182, December.
    18. Marie Albertine Djuikom, 2018. "Incentives to labour migration and agricultural productivity: The Bayesian perspective," WIDER Working Paper Series wp-2018-45, World Institute for Development Economic Research (UNU-WIDER).
    19. Yu, Jun, 2005. "On leverage in a stochastic volatility model," Journal of Econometrics, Elsevier, vol. 127(2), pages 165-178, August.
    20. Li Mingliang & Tobias Justin, 2005. "Bayesian Modeling of School Effects Using Hierarchical Models with Smoothing Priors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(3), pages 1-33, September.

    More about this item

    Keywords

    ECONOMIC MODELS ; ESTIMATOR;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fth:hertbu:2000:7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/echeruk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.