Abstract. Background: As more and more people suffer from sleep disorders, the need to develop an efficient, inexpensive, and accurate assessment tool for screening sleep disorders has become more urgent. Aim: The aim of the current study was to develop a system allowing computerized adaptive testing for sleep disorders (CAT-SD). Methods: A large sample ( N = 1,304) was recruited to construct an item bank for CAT-SD and to investigate the psychometric characteristics of CAT-SD. First, analyses of unidimensionality, model fit, item fit, item discrimination parameters, and differential item functioning (DIF) were conducted to construct a final item pool to meet the requirements of item response theory measurement. Then, a simulated CAT study with real data was performed to investigate the psychometric characteristics of CAT-SD, including the reliability, validity, and predictive utility (sensitivity and specificity). Results: The final unidimensional item bank of the CAT-SD had good item fit, high discrimination, and no DIF. Moreover, it had acceptable reliability, validity, and predictive utility. Limitations: Non-statistical assembly constraints, execution environment, construction of item bank, criterion-related validity, and predictive utility (sensitivity and specificity) of CAT-SD, and sample representativeness are discussed. Conclusions: The CAT-SD could be used as an effective and accurate assessment tool for measuring the sleep disorders in individuals and offers a novel approach to the screening of sleep disorders utilizing psychological scales.

As schizotypal personality disorder (SPD) increasingly prevails in the general population, a rapid and comprehensive measurement instrument is imperative to screen individuals at risk for SPD. To address this issue, we aimed to develop a computerized adaptive testing for SPD (CAT-SPD) using a non-clinical Chinese sample (N = 999), consisting of a calibration sample (N1 = 497) and a validation sample (N2 = 502). The item pool of SPD was constructed from several widely used SPD scales and statistical analyses based on the item response theory (IRT) via a calibration sample using a graded response model (GRM). Finally, 90 items, which measured at least one symptom of diagnostic criteria of SPD in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and had local independence, good item fit, high slope, and no differential item functioning (DIF), composed the final item pool for the CAT-SPD. In addition, a simulated CAT was conducted in an independent validation sample to assess the performance of the CAT-SPD. Results showed that the CAT-SPD not only had acceptable reliability, validity, and predictive utility but also had shorter but efficient assessment of SPD which can save significant time and reduce the test burden of individuals with less information loss.

Background: As more and more people suffer from sleep disorders, developing an efficient, cheap and accurate assessment tool for screening sleep disorders is becoming more urgent. This study developed a computerized adaptive testing for sleep disorders (CAT-SD). Methods: A large sample of 1,304 participants was recruited to construct the item pool of CAT-SD and to investigate the psychometric characteristics of CAT-SD. More specifically, firstly the analyses of unidimensionality, model fit, item fit, item discrimination parameter and differential item functioning (DIF) were conducted to construct a final item pool which meets the requirements of item response theory (IRT) measurement. In addition, a simulated CAT study with real response data of participants was performed to investigate the psychometric characteristics of CAT-SD, including reliability, validity and predictive utility (sensitivity and specificity). Results: The final unidimensional item bank of the CAT-SD not only had good item fit, high discrimination and no DIF; Moreover, it had acceptable reliability, validity and predictive utility. Conclusions: The CAT-SD could be used as an effective and accurate assessment tool for measuring individuals' severity of the sleep disorders and offers a bran-new perspective for screening of sleep disorders with psychological scales.