Doing less but getting more: Improving forced-choice measures with IRT.

Multidimensional forced-choice (MFC) questionnaires typically show good validities and are resistant to impression management effects. However, they yield ipsative data, which distorts scale relationships and makes comparisons between people problematic. Depressed reliability estimates also led developers to create tests of potentially excessive length. We apply an IRT Preference Model to make more efficient use of information in existing MFC questionnaires. OPQ32i used for selection and assessment internationally is examined using this approach. The latent scores recovered from a much reduced number of MFC items are superior to the full test?s ipsative scores, and comparable to unbiased normative scores.