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Item Parameter Estimation Via Marginal Maximum Likelihood and an EM Algorithm: A Didactic

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  • Michael R. Harwell
  • Frank B. Baker
  • Michael Zwarts

Abstract

The Bock and Aitkin (1981) Marginal Maximum Likelihood/EM approach to item parameter estimation is an alternative to the classical joint maximum likelihood procedure of item response theory. Unfortunately , the complexity of the underlying mathematics and the terse nature of the existing literature has made understanding of the approach difficult. To make the approach accessible to a wider audience, the present didactic paper provides the essential mathematical details of a marginal maximum likelihood/EM solution and shows how it can be used to obtain consistent item parameter estimates. For pedagogical purposes, a short BASIC computer program is used to illustrate the underlying simplicity of the method.

Suggested Citation

  • Michael R. Harwell & Frank B. Baker & Michael Zwarts, 1988. "Item Parameter Estimation Via Marginal Maximum Likelihood and an EM Algorithm: A Didactic," Journal of Educational and Behavioral Statistics, , vol. 13(3), pages 243-271, September.
    • Handle: RePEc:sae:jedbes:v:13:y:1988:i:3:p:243-271
    DOI: 10.3102/10769986013003243
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    Cited by:

    1. Norman Rose & Matthias Davier & Benjamin Nagengast, 2017. "Modeling Omitted and Not-Reached Items in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 795-819, September.
    2. Alexander Weissman, 2013. "Optimizing information using the EM algorithm in item response theory," Annals of Operations Research, Springer, vol. 206(1), pages 627-646, July.

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