An Investigation of Sample Size Splitting on ATFIND and DIMTEST

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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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Modeling multidimensional test data with a unidimensional model can result in seri-ous statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND pro-cedure for empirically deriving a subtest composed of items that potentially measure a second dimension versus DIMTEST for assessing whether this subtest represents a second dimension were investigated. Conditions explored included proportion of sample used for ATFIND, sample size, test length, interability correlations, test struc-ture, and distribution of item difficulties. Overall, it appears that DIMTEST has Type I error rates near the nominal rate and good power in detecting multidimensionality, although Type I error inflation is observed for larger sample sizes. Results suggest that a 50/50 split maximizes power and keeps the Type I error rate below the nom-inal level unless the test is short and the sample is large. A 75/25 split controls Type I error better for short tests and large samples.