Developing and Validating an Item Bank for Alcohol Use Disorder Screening in the Chinese Population by Using the Computerized Adaptive Testing

Abstract

Objective To detect the individual’s severity of alcohol use disorder (AUD) in an effective and accurate manner, this study aimed to build an item bank for AUD screening and derive the computerized adaptive testing (CAT) version of AUD (CAT-AUD). Methods The initial CAT-AUD item bank was selected from the Chinese version of the questionnaires related to AUD according to the DSM-5 criteria. Then 915 valid Chinese samples, covering the healthy individuals and the AUD high-risk individuals, completed the initial CAT-AUD item bank. By testing the unidimensionality, test fit, item fit, discrimination parameter and differential item functioning of the initial item bank, the final CAT-AUD item bank confirming to the requirements of the item response theory (IRT) were obtained. Subsequently, the CAT-AUD simulation study based on the real data of the final item bank conducted to detect characteristics, reliability, validity, and predictive utility (sensitivity and specificity) of CAT-AUD. Results The CAT-AUD item bank meeting the IRT psychometric measurement requirements could be well geared into the graded response model. The Pearson’s correlation between the estimated theta via CAT-AUD and the estimated theta via the full-length item bank reached 0.95, and the criterion-related validity was 0.63. CAT-AUD can provide highly reliable test results for subjects whose theta above −0.8 with an average of 16 items. Besides, the predictive utility of CAT-AUD was better than AUDIT and AUDIT-C. Conclusion In brief, the CAT-AUD developed in this study can effectively screen the AUD high-risk group and accurately measure the AUD severity of individuals.

Applicability and Efficiency of a Computerized Adaptive Test for the Washington Assessment of the Risks and Needs of Students

“…Each item was chosen from any subdomain as long as it was not previously administered to the same simulee, and it was the most informative item in the currently estimated θ level. Any trait estimator and item selection criteria can be used together for a given item pool, but one of the most commonly used combinations is the EAP estimator with the MFI item selection method (e.g., Bulut & Kan, 2012; Lian et al, 2020; Tan et al, 2018). MFI is the only method currently available in catR for polytomous IRT models (Magis & Raîche, 2012).…”