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- W2059604453 abstract "DETECT is a nonparametric methodology to identify the dimensional structure underlying test data. The associated DETECT index, D max , denotes the degree of multidimensionality in data. Conditional covariances (CCOV) are the building blocks of this index. In specifying population CCOVs, the latent test composite θ TT is used as the conditional variable. In estimating the CCOVs, the total test score of all items in the test ( T) or the rest score of remaining items ( S) are generally used as estimates of the latent composite θ TT . However, estimated CCOVs are biased when using T or S as a proxy for θ TT . Some type of correction is needed to adjust this bias. This study was an investigation of different ways to estimate the DETECT index based on the conditional scores T and S, and additional bias adjustments, resulting in six different estimates, D 1 through D 6 . These six indices were investigated in simulated settings, varying the test length, sample size, and the degree of multidimensionality (108 in all). The results showed that indices D 1 , D 2 , and D 5 are not acceptable as they displayed highly inflated D max values. No statistically significant differences were found between indices D 3 and D 6 . Overall comparison of indices D 3 with D 4 showed that even though they differed significantly in D max values, there was no practically meaningful difference between the performance of these two indices. For these reasons, it is recommended that the current index D 3 be retained even though index D 4 displayed slightly better results in the current study." @default.
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- W2059604453 date "2010-10-14" @default.
- W2059604453 modified "2023-09-26" @default.
- W2059604453 title "A Comparison of Bias Correction Adjustments for the DETECT Procedure" @default.
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- W2059604453 doi "https://doi.org/10.1177/0146621610376767" @default.
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