Rasch analysis of the Chinese version of the Clinically Useful Depression Outcome Scale in patients with major depressive disorder

Author:

Zhong Jing1,Ma Hai-Yan2,Wang Xue-Mei3,Huang Xiao-Jie4,Xu Ming-Zhi4

Affiliation:

1. Southern Medical University

2. Guangdong second provincial general hospital

3. Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou University of Chinese Medicine

4. Guangdong Academy of Medical Sciences

Abstract

Abstract Objective To examine the psychometric properties of the Chinese version of the Clinically Useful Depression Outcome Scale (CUDOS) in the Chinese patients with major depressive disorder (MDD) using Rasch analysis. Method The sample consisted of 283 patients with MDD (69% females). The Rasch model was applied to examine the overall fit of the Chinese version of CUDOS and the fit of the 18 items to the Rasch model. Dimensionality, item-model fit, differential item functioning (DIF), reliability, ordering of response categories and targeting were tested to examine the psychometric properties of the Chinese version of CUDOS. Results Rasch analysis demonstrated the unidimensionality of the Chinese version of CUDOS. Of the 18 items, three items (item 4, item 5, item 6) showed misfit in the model. After merging item 4 into item 3 and item 6 into item 5, the overall model fit improved. The person separation index (PSI) was 3.0 and the person reliability coefficient was 0.90. No evidence of significant differential item functioning(DIF) was found when associated with gender and age. No disordered category and threshold of the rating response were observed, which meant the response categories setting was reasonable. The mean ability of person was − 0.53. Conclusion The results suggested that the Chinese version of CUDOS has acceptable psychometric properties. In order to improve the quality and applicability of the Chinese version of CUDOS, the merging of item 4 into item 3 and item 6 into item 5 are suggested.

Publisher

Research Square Platform LLC

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