On the basis of monte carlo runs, in which item response data were generated for a variety of test characteristics, procedures for estimating item and ability parameters for homogeneous, uni dimensional tests are developed on the assumption that values of the slope parameter a and the guess ing parameter c are constant over items. The procedures focus on estimates of the a parameter, regarded as an important statistic for characteriz ing an ability. This parameter is estimated from person characteristic functions for different levels of the total raw score distribution. The procedures can be applied to datasets with relatively small or very large Ns and with either relatively small or large numbers of items. They are illustrated with data from several cognitive ability tests.
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