Computerized Test Construction Using an Average Growth Approximation of Target Information Functions.

This paper describes the derivation of several item selection algorithms for use in fitting test items to target information functions. These algorithms circumvent iterative solutions by using the criteria of moving averages of the distance to a target information function and simultaneously considering an entire range of ability points used to condition the information functions. The algorithms were implemented in a microcomputer software package and 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 provide reliable fit to the target in terms of item parameters, test information functions and expected score distributions. A discussion of the application is included.