Estimation in Latent Trait Models.

Abstract

Estimation of ability and item parameters in latent trait models is discussed. When both ability and item parameters are considered fixed but unknown, the method of maximum likelihood for the logistic or probit models is well known. This paper discusses techniques for estimating ability and item parameters when the ability parameters, or item parameters (or both) are considered random. When the item parameters are considered fixed, and the ability parameters are random, from some prior distribution with fixed but unknown parameters, the EM algorithm is applied. A modification of the EM algorithm, which requires considerably less computation, is proposed. When both ability and item parameters are considered random, the EM algorithm seems to be impractical because the amount of computation needed is very large. In this case another modification to the EM algorithm is proposed.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1981
Accession Number
ADA102216

Entities

People

  • Robert K. Tsutakawa
  • Steven E. Rigdon

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Behavioral Sciences
  • Cognition
  • Computations
  • Computer Science
  • Data Sets
  • Education
  • Manpower Utilization
  • Military Research
  • Naval Operations
  • Navy
  • Personnel Management
  • Psychology
  • Social Sciences
  • Statistical Samples
  • Statistics
  • Uss Carl Vinson

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Psychometric Testing or Psychological Assessment.