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. Index terms: algorithmic test construction,
automated test assembly, greedy algorithm, heuristic
algorithm, item response theory, marginal maximum
likelihood, mathematical programming, simulation,
Armstrong, R. D, Jones, D. H, Li, Xuan & Wu, Ing-Long. (1996). A study of a network-flow algorithm and a noncorrecting algorithm for test assembly. Applied Psychological Measurement, 20, 89-98. doi:10.1177/014662169602000108
Armstrong, R. D.; Jones, D. H.; Li, Xuan; Wu, Ing-Long.
A study of a network-flow algorithm and a noncorrecting algorithm for test assembly.
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