For tests consisting of multiple subtests, unidimensional item response theory (IRT) models apply when the subtests are known to measure a common underlying ability. However, in many instances, due to the lack of a satisfactory index for assessing the dimensionality assumption, the test structure is not clear. A more general IRT model, the multiunidimensional model, is more flexible and efficient in various test situations. This article compares these two classes of normal ogive two-parameter models and shows that the multiunidimensional model offers a better way to represent test situations not realized in unidimensional models.
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