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The computerized adaptive testing iteratively administers items based on the participant's responses to previous questions and therefore the computerized adaptive testing requires very few items to provide a precise and accurate assessment of patient-reported outcome measures [24, 25].
The examiner observes and administers items and the test takes 10 30 min to complete.
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However, administering items using a pretest is costly, and there is a risk of exposing the items before they are used for a real test.
We calibrated a PF item bank via item response theory (IRT), administered items with a post hoc CAT design, and determined whether CAT would improve accuracy and precision of score estimates over random item selection.
An item that exhibits DIF can also have large repercussions in a CAT since the sequence of administered items to the respondents in part is dependent on their responses to that item (Makransky and Glas 2013).
This comprises the management of the item pool and storing all information necessary to carry out a test (e.g., storing the history of administered items and their answers).
We further note that item dependencies within the students' responses were neglectable (for further information, refer to Scherer 2012).The IRT scaling was applied in two steps: The first step included an analysis based on all administered items with the aim of identifying items which did not fit the IRT model.
Domain-specific item management inherits its functionality from the core component and extends it in terms of domain-specific user support measures, i.e., the provision of supporting information that should be made available to examinees when answering the administered items.
Administered items (order, dimension, number in item bank, text and given answer).
Naturally, when increasing the required measurement precision, the average number of administered items increased.
Theta scores and standard errors per dimension for each of the administered items.
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