, Volume 46, Issue 4, pp 443-459

First online:

Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm

  • R. Darrell BockAffiliated withDepartment of Behavioral Sciences, The University of Chicago
  • , Murray AitkinAffiliated withUniversity of Lancaster

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Maximum likelihood estimation of item parameters in the marginal distribution, integrating over the distribution of ability, becomes practical when computing procedures based on an EM algorithm are used. By characterizing the ability distribution empirically, arbitrary assumptions about its form are avoided. The Em procedure is shown to apply to general item-response models lacking simple sufficient statistics for ability. This includes models with more than one latent dimension.

Key words

estimation of item parameters EM algorithm item analysis latent trait dichotomous factor analysis Law School Aptitude Test (LSAT)