The derivations of several item selection
algorithms for use in fitting test items to target
information functions (IFS) are described. These
algorithms circumvent iterative solutions by using
the criteria of moving averages of the distance to a
target IF and by simultaneously considering an
entire range of ability points used to condition the
IFS. The algorithms were tested by generating six
forms of an ACT math test, each fit to an existing
target test, including content-designated item subsets.
The results indicate that the algorithms provided
reliable fit to the target in terms of item
parameters, test information functions, and
expected score distributions. Index terms: computerized
testing, information functions, item information,
parallel tests, test construction, test information.
Luecht, Richard M & Hirsch, Thomas M. (1992). Item selection using an average growth approximation of target information functions. Applied Psychological Measurement, 16, 41-51. doi:10.1177/014662169201600104
Luecht, Richard M.; Hirsch, Thomas M..
Item selection using an average growth approximation of target information functions.
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