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Advances in Modeling Model Discrepancy: Comment on Wu and Browne (2015)

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  • Robert MacCallum
  • Anthony O’Hagan

Abstract

Wu and Browne (Psychometrika, 79, 2015 ) have proposed an innovative approach to modeling discrepancy between a covariance structure model and the population that the model is intended to represent. Their contribution is related to ongoing developments in the field of Uncertainty Quantification (UQ) on modeling and quantifying effects of model discrepancy. We provide an overview of basic principles of UQ and some relevant developments and we examine the Wu–Browne work in that context. We view the Wu–Browne contribution as a seminal development providing a foundation for further work on the critical problem of model discrepancy in statistical modeling in psychological research. Copyright The Psychometric Society 2015

Suggested Citation

  • Robert MacCallum & Anthony O’Hagan, 2015. "Advances in Modeling Model Discrepancy: Comment on Wu and Browne (2015)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 601-607, September.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:3:p:601-607
    DOI: 10.1007/s11336-015-9452-2
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    References listed on IDEAS

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    1. Ledyard Tucker & Raymond Koopman & Robert Linn, 1969. "Evaluation of factor analytic research procedures by means of simulated correlation matrices," Psychometrika, Springer;The Psychometric Society, vol. 34(4), pages 421-459, December.
    2. Hao Wu & Michael Browne, 2015. "Quantifying Adventitious Error in a Covariance Structure as a Random Effect," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 571-600, September.
    3. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
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    Cited by:

    1. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.

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