Abstract
Many long-term testing programs rely on large item banks that need to be replenished regularly with new items, and these new items need to be pretested before being used operationally. Online calibration is a pretesting strategy in computerised adaptive testing, which embeds pretest items in operational tests and adaptively matches the pretest items with examinees. This paper compares five existing methods for pretest item selection in online calibration. A simulation study was conducted under the one-, two-, and three-parameter logistic models. The effects of two estimation methods, three seeding locations, and five calibration sample sizes were also investigated. Findings from the simulation study are mixed. However, overall, the simplest random selection method appears to be a potential best choice.
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