An Illustration of the Effects of Ignoring a Secondary Factor

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

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The purpose of this brief report is to illustrate how a small proportion of items measuring a secondary factor can have a large impact on the misestimation of the a-parameters for all items. In this real dataset, when the model was specified as unidimensiona the a-parameters for the items tapping the secondary construct were overestimated and the a-parameters for the other items tended to be underestimated.