A study of a network-flow algorithm and a noncorrecting algorithm for test assembly

R. D. Armstrong, Douglas H. Jones, Xuan Li

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

The network-flow algorithm (NFA) of Armstrong, Jones, & Wu (1992) and the average growth approximation algorithm (AGAA) of Luecht & Hirsch (1992) were evaluated as methods for automated test assembly. The algorithms were used on ACT and ASVAB item banks, with and without error in the item parameters. Both algorithms matched a target test information function on the ACT item bank, both before and after error was introduced. The NFA matched the target on the ASVAB item bank; however, the AGAA did not, even without error in this item bank. The AGAA is a noncorrecting algorithm, and it made poor item selections early in the search process when using the ASVAB item bank. The NFA corrects for nonoptimal choices with a simplex search. The results indicate that reasonable error in item parameters is not harmful for test assembly using the NFA or AGAA on certain types of item banks.

Original languageEnglish (US)
Pages (from-to)89-98
Number of pages10
JournalApplied Psychological Measurement
Volume20
Issue number1
DOIs
StatePublished - Mar 1996

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)

Keywords

  • Algorithmic test construction
  • Automated test assembly
  • Greedy algorithm
  • Heuristic algorithm
  • Item response theory
  • Marginal maximum likelihood
  • Mathematical programming
  • Simulation
  • Test construction

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