Latent Trait Model Contributions to Criterion-Referenced Testing Technology.

Abstract

The goals and results of an 18-month study addressing latent trait model applications to measurement problems arising in criterion-referenced testing are presented. The research studies described in the report cover the following areas: (a) problems with classical test models; introduction to latent trait models, features, assumptions, parameter estimation, and test and item information curves; building tests with latent trait models; (b) latent ability scales - uses, interpretations, and properties; equating test scores for using a common set of norms tables; approaches for addressing the goodness of fit between a latent trait model and a data set; (c) comparing the one-parameter and three-parameter logistic models for ability estimation and decision-making with several test lengths and ability levels; (d) determining the optimal length of criterion-referenced tests with different types of item pools (varying the level of item heterogeneity) and using two different item selection methods; (e) a system to allow instructors to specify required level of measurement precision and obtain information to help them determine test length; (f) comparing the fit of the one-parameter and three-parameter models to 25 sets of test data; and (g) building banks of valid test items. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1982
Accession Number
ADA112048

Entities

People

  • Ronald K. Hambleton

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Facilities
  • Computer Programs
  • Computer Simulations
  • Computers
  • Consistency
  • Data Sets
  • Education
  • Governments
  • Instructors
  • Mathematical Models
  • Reliability
  • Simulations
  • Standards
  • Statistics
  • Students

Readers

  • Instructional Design and Training Evaluation.
  • Regression Analysis.
  • Statistical inference.