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Estimation of Expected Fisher Information for IRT Models

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  • Scott Monroe

    (University of Massachusetts Amherst)

Abstract

In item response theory (IRT) modeling, the Fisher information matrix is used for numerous inferential procedures such as estimating parameter standard errors, constructing test statistics, and facilitating test scoring. In principal, these procedures may be carried out using either the expected information or the observed information. However, in practice, the expected information is not typically used, as it often requires a large amount of computation. In the present research, two methods to approximate the expected information by Monte Carlo are proposed. The first method is suitable for less complex IRT models such as unidimensional models. The second method is generally applicable but is designed for use with more complex models such as high-dimensional IRT models. The proposed methods are compared to existing methods using real data sets and a simulation study. The comparisons are based on simple structure multidimensional IRT models with two-parameter logistic item models.

Suggested Citation

  • Scott Monroe, 2019. "Estimation of Expected Fisher Information for IRT Models," Journal of Educational and Behavioral Statistics, , vol. 44(4), pages 431-447, August.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:4:p:431-447
    DOI: 10.3102/1076998619838240
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    References listed on IDEAS

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    Cited by:

    1. Scott Monroe, 2021. "Testing Latent Variable Distribution Fit in IRT Using Posterior Residuals," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 374-398, June.

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