Most item selection in computerized adaptive testing
is based on Fisher information (or item information). At
each stage, an item is selected to maximize the Fisher
information at the currently estimated trait level (θ).
However, this application of Fisher information could be
much less efficient than assumed if the estimators are
not close to the true θ, especially at early stages of an
adaptive test when the test length (number of items) is
too short to provide an accurate estimate for true θ. It is
argued here that selection procedures based on global
information should be used, at least at early stages of a
test when θ estimates are not likely to be close to the
true θ. For this purpose, an item selection procedure
based on average global information is proposed. Results
from pilot simulation studies comparing the usual
maximum item information item selection with the proposed
global information approach are reported, indicating
that the new method leads to improvement in terms
of bias and mean squared error reduction under many
circumstances. Index terms: computerized adaptive
testing, Fisher information, global information, information
surface, item information, item response theory,
Kullback-Leibler information, local information, test information.
Chang, Hua-Hua & Ying, Zhiliang. (1996). A global information approach to computerized adaptive testing. Applied Psychological Measurement, 20, 213-229. doi:10.1177/014662169602000303
Chang, Hua-Hua; Ying, Zhiliang.
A global information approach to computerized adaptive testing.
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