Full-Information Item Factor Analysis.

Abstract

A method of item factor analysis based on Thurstone's multiple factor model and implemented by marginal maximum likelihood estimation and the EM algorithm is described. Statistical significance of successive factors added to the model is tested by the likelihood ratio criterion. Provisions for effects of guessing on multiple choice items, and for omitted and not reached items, are included. Bayes constraints on the factor loadings are found to be necessary to suppress Heywood cases. Numerous applications to simulated and real data are presented to substantiate the accuracy and practical utility of the method. Analysis of the power tests of the Armed Services Vocational Aptitude Battery shows statistically significant departures from unidimensionality in five of the eight tests. Additional keywords: Tables(data); Aptitude tests; Simulation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1985
Accession Number
ADA159135

Entities

People

  • E. Muraki
  • R. D. Bock
  • R. Gibbons

Organizations

  • NORC at the University of Chicago

Tags

Communities of Interest

  • Biomedical
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Biological Sciences
  • Cognition
  • Cognitive Systems Engineering
  • Computational Science
  • Databases
  • Economic Analysis
  • Educational Psychology
  • Factor Analysis
  • Illinois
  • Information Science
  • Maximum Likelihood Estimation
  • Military Research
  • New York
  • Personnel Management
  • Probability
  • Psychology
  • United States

Fields of Study

  • Education

Readers

  • Computational Modeling and Simulation
  • Psychometric Testing or Psychological Assessment.
  • Regression Analysis.