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On the Estimation of Hierarchical Latent Regression Models for Large-Scale Assessments

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  • Deping Li
  • Andreas Oranje
  • Yanlin Jiang

Abstract

To find population proficiency distributions, a two-level hierarchical linear model may be applied to large-scale survey assessments such as the National Assessment of Educational Progress (NAEP). The model and parameter estimation are developed and a simulation was carried out to evaluate parameter recovery. Subsequently, both a hierarchical and a simple model were applied to NAEP reading data. The impact of using a hierarchical model was found to be relatively modest in this case, mostly due to modest clustering. Several other applications and future studies are discussed.

Suggested Citation

  • Deping Li & Andreas Oranje & Yanlin Jiang, 2009. "On the Estimation of Hierarchical Latent Regression Models for Large-Scale Assessments," Journal of Educational and Behavioral Statistics, , vol. 34(4), pages 433-463, December.
    • Handle: RePEc:sae:jedbes:v:34:y:2009:i:4:p:433-463
    DOI: 10.3102/1076998609332757
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    References listed on IDEAS

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    1. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    2. D. Pfeffermann & C. J. Skinner & D. J. Holmes & H. Goldstein & J. Rasbash, 1998. "Weighting for unequal selection probabilities in multilevel models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 23-40.
    3. Robert Mislevy, 1984. "Estimating latent distributions," Psychometrika, Springer;The Psychometric Society, vol. 49(3), pages 359-381, September.
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    Cited by:

    1. Brian Junker & Lynne Schofield & Lowell Taylor, 2012. "The use of cognitive ability measures as explanatory variables in regression analysis," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-19, December.
    2. Lynne Schofield & Brian Junker & Lowell Taylor & Dan Black, 2015. "Predictive Inference Using Latent Variables with Covariates," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 727-747, September.
    3. Andreas Oranje & Andrew Kolstad, 2019. "Research on Psychometric Modeling, Analysis, and Reporting of the National Assessment of Educational Progress," Journal of Educational and Behavioral Statistics, , vol. 44(6), pages 648-670, December.
    4. Minjeong Jeon & Sophia Rabe-Hesketh, 2012. "Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures," Journal of Educational and Behavioral Statistics, , vol. 37(4), pages 518-542, August.
    5. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2021. "On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 430-465, August.

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