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Using Pooled Heteroskedastic Ordered Probit Models to Improve Small-Sample Estimates of Latent Test Score Distributions

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Listed:
  • Benjamin R. Shear

    (1877University of Colorado-Boulder)

  • Sean F. Reardon

    (6429Stanford University Graduate School of Education)

Abstract

This article describes an extension to the use of heteroskedastic ordered probit (HETOP) models to estimate latent distributional parameters from grouped, ordered-categorical data by pooling across multiple waves of data. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in each of a small number of ordered “proficiency†levels. HETOP models can be used to estimate means and standard deviations of the underlying (latent) test score distributions but may yield biased or very imprecise estimates when group sample sizes are small. A simulation study demonstrates that the pooled HETOP models described here can reduce the bias and sampling error of standard deviation estimates when group sample sizes are small. Analyses of real test score data demonstrate the use of the models and suggest the pooled models are likely to improve estimates in applied contexts.

Suggested Citation

  • Benjamin R. Shear & Sean F. Reardon, 2021. "Using Pooled Heteroskedastic Ordered Probit Models to Improve Small-Sample Estimates of Latent Test Score Distributions," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 3-33, February.
    • Handle: RePEc:sae:jedbes:v:46:y:2021:i:1:p:3-33
    DOI: 10.3102/1076998620922919
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    References listed on IDEAS

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    Cited by:

    1. Daniel F. McCaffrey & Steven A. Culpepper, 2021. "Introduction to JEBS Special Issue on NAEP Linked Aggregate Scores," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 135-137, April.

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