Procedures for Criterion Referenced Tailored Testing.

Abstract

This report summarizes the research findings of a four-year contract investigating the applicablity of item response theory and tailored testing to criterion-referenced measurement. Six major areas were studied on the project. These included: (a) techniques for forming unidimensional item sets, (b) techniques for calibrating items, (c) item parameter linking procedures, (d) comparisons of latent trait models, (e) tailored testing procedures, and (f) decision making procedures. The results showed that factor analytic procedures were best at forming unidimensional item pools, the LOGIST calibration program performed slightly better than the ANCILLES program for item calibration, the maximum likelihood procedure using the LOGIST program generally gave the best linking, the three-parameter logistic model was preferred to the one-parameter model for tailored testing applications, the maximum likelihood based tailored testing procedure was slightly preferred to the Owen's Bayesian based procedure, and the use of the sequential probability ratio test with tailored testing resulted in substantial savings in test length. Overall, tailored testing was shown to be feasible for achievement testing applications. More detailed results are described in the papers and reports listed in this report. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1981
Accession Number
ADA107135

Entities

People

  • Mark D. Reckase

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Behavioral Sciences
  • Calibration
  • Computer Science
  • Education
  • Educational Psychology
  • Manpower Utilization
  • Military Research
  • Naval Operations
  • Navy
  • Personnel Management
  • Probability
  • Psychology
  • Reliability
  • Social Sciences
  • Students
  • Test And Evaluation
  • Uss Carl Vinson

Readers

  • Psychometric Testing or Psychological Assessment.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference