Item Selection Using an Average Growth Approximation of Target Information Functions

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Abstract
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 sub sets. The results indicate that the algorithms pro vided reliable fit to the target in terms of item parameters, test information functions, and expected score distributions.