In the present paper, linear programming was used
to select items from item pools based on one-, two-,
and three-parameter models so that a target test information
function was reached. The primary interest was
in the distributional characteristics of the items thus
selected. The results suggest that the linear programming
approach focuses on the "worst feature" of the
target information function (i.e., the extremes of a
uniform target and the maximum of a peaked target).
The values of the parameters of the selected items
tend to form clusters. For uniform targets, these clusters
are associated with the extremes of the target
range, whereas for peaked targets they are associated
with the maximum of the target. Selecting items from
an item pool by linear programming appears to be a
useful addition to the test constructor’s repertoire.
However, additional refinement may be needed to obtain
a specific distribution of item parameters for a
given test. Index terms: Item response theory,
Item selection, Linear programming, Target information
Baker, Frank B, Cohen, Alan S & Barmish, B. Ross. (1988). Item characteristics of tests constructed by linear programming. Applied Psychological Measurement, 12, 189-199. doi:10.1177/014662168801200208
Baker, Frank B.; Cohen, Alan S.; Barmish, B. Ross.
Item characteristics of tests constructed by linear programming.
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