Abstract
Two strategies for assessing item bias are discussed: methods that compare (transformed) item difficulties unconditional on ability level and methods that compare the probabilities of correct response conditional on ability level. In the present study, the logit model was used to compare the probabilities of correct response to an item by members of two groups, these probabilities being conditional on the observed score. Here the observed score serves as an indicator of ability level. The logit model was iteratively applied: In the Tth iteration, the T items with the highest value of the bias statistic are excluded from the test, and the observed score indicator of ability for the (T + 1)th iteration is computed from the remaining items. This method was applied to simulated data. The results suggest that the iterative logit method is a substantial improvement on the noniterative one, and that the iterative method is very efficient in detecting biased and unbiased items.
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