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A new sharing item response theory (SIRT) model is
presented which explicitly models the effects of sharing item content
between informants and test-takers. This model is used to construct adaptive
item selection and scoring rules that provide increased precision and
reduced score gains in instances where sharing occurs. The adaptive item
selection rules are expressed as functions of the item's exposure rate in
addition to other commonly used properties (characterized by difficulty,
discrimination, and guessing parameters). Based on the results of simulated
item responses, the new item selection and scoring algorithms compare
favorably to the Sympson-Hetter exposure control method. The new SIRT
approach provides higher reliability and lower score gains in instances
where sharing occurs. |
