Detecting Multidimensionality Due to Curricular Differences

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Data were generated to simulate multidimensionality resulting from including two or four subtopics on a test. Each item was dependent on an ability trait due to instruction and learning, which was the same across all items, as well as an ability trait unique to the subtopic of the test (such as biology on a general science test). The eigenvalues of the item correlation matrix and Yen's Q3 were not greatly influenced by multidimensionality under conditions where the responses of a large proportion of students shared the influence of common instruction across subtopics. In contrast, Stout's T procedure was effective at detecting this type of multidimensionality, unless the subtopic abilities were correlated.

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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.