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December 1, 1981Psychometrika

Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm

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Authors

RBR. Darrell BockUniversity of California, Santa BarbaraMAMurray AitkinThe University of Melbourne

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Implication

Randomized trial demonstrates effective estimation of item parameters using an EM algorithm, avoiding assumptions about ability distribution.

Key Points

  • To effectively estimate item parameters using marginal maximum likelihood without making arbitrary assumptions about the ability distribution.
  • Utilized an EM algorithm for maximum likelihood estimation of item parameters in marginal distribution.
  • Characterized ability distribution empirically to avoid assumptions about its form.
  • Applied the procedure to general item-response models with multiple latent dimensions.
  • The EM algorithm effectively estimates item parameters across various item-response models.
  • Demonstrated applicability in models lacking simple sufficient statistics for ability.
  • Successfully handled scenarios with more than one latent dimension.

Cite This Study

Bock et al. (1981) studied this question.

synapsesocial.com/papers/69d7cfe733ca018b39ae2e83https://doi.org/10.1007/bf02293801
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Estimating Item Parameters and Latent Ability when Responses are Scored in Two or More Nominal Categories1972 · 1,239 citations
  2. 2Marginal Maximum Likelihood Estimation for the One-Parameter Logistic Model1982 · 184 citations
  3. 3Estimating the Parameters of the Latent Population Distribution1977 · 112 citations
  4. 4Solving Implicit Equations in Psychometric Data Analysis1975 · 44 citations
  5. 5Discrete Statistical Models With Social Science Applications.1983 · 324 citations