Introduction: The Davos Assessment of Cognitive Biases Scale (DACOBS) is widely used to assess cognitive biases in patients who have schizophrenia. However, the lack of a modified Chinese-language version of the DACOBS (MCL-DACOBS) precludes Chinese schizophrenic patients from treatment aimed at normalizing cognitive biases, impacting their prognosis. Here, we aimed to produce a DACOBS for China and test the validity and reliability of the resultant MCL-DACOBS.
Methods: Eighteen researchers collaborated to develop the MCL-DACOBS: A total of 15 researchers modified and translated the English version of the DACOBS, 1 native-English-speaking researcher back-translated the scale, and 2 Chinese sinologists localized and optimized the language of the MCL-DACOBS. Forty-two volunteers checked the scale items' comprehensibility, and the two sinologists performed further localization and optimization based on their feedback. The final version of the MCL-DACOBS used in this study was thus derived from the harmonized English-language version of the scale. Confirmatory factor analyses (CFAs) were used to examine the best latent structure of the MCL-DACOBS. Cronbach's α and intraclass correlation coefficients (ICCs) were used to check the reliability. The discriminative ability of the MCL-DACOBS was assessed according to the area under the receiver operating characteristic curve.
Results: The CFA showed that all items loaded onto factors with loadings >0.400. A two-factor structure showed a good model fit (root mean square error of approximation = .018, Tucker-Lewis index = .978, comparative fit index = .984). Promax rotation demonstrated that each item had a high factor load (0.432-0.774). Cronbach's α coefficient and ICC for the MCL-DOCABS were .965 and .957, respectively, indicating that the scale has ideal reliability.
Conclusion: The MCL-DACOBS has good validity and good reliability, and its psychometric properties indicate that it is a valid tool for measuring cognitive biases in Chinese patients with schizophrenia.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570469 | PMC |
http://dx.doi.org/10.1002/brb3.3185 | DOI Listing |
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