Development of a Computerized Adaptive Test for Separation Anxiety Disorder Among Adolescents

Abstract

Background: Separation anxiety disorder (SAD) is one of the most common mental disorders among children and adolescents, and it may seriously affect their growth, daily life, and learning. Self-report scales have been used for diagnosis, which require lengthy testing and personnel. Methods: A total of 1,241 adolescents were recruited from 16 junior-and seniorhigh schools in China. The initial item bank was selected from classical SAD scales according to the DSM-5. First, the optimal model was selected using item response theory (IRT) according to data fit. Then, per the IRT analysis, items that did not meet the psychometric requirements were deleted (e.g., discriminating values < 0.2). Consequently, a computerized adaptive test (CAT) for SAD was formed (CAT-SAD). Results: An average of 17 items per participant was required to achieve and maintain a 0.3 standard error of measurement in the SAD severity estimate. The estimated correlation of the CAT-SAD with the total 68-item test score was 0.955. CAT-SAD scores were strongly related to the probability of a SAD diagnosis with the Separation Anxiety Assessment Scale-Child and Adolescent Version. Therefore, SAD could be accurately predicted by the CAT-SAD. Conclusions: Exploratory factor analyses revealed that SAD was unidimensional. The CAT-SAD, which has good reliability and validity and high sensitivity and specificity, provides an efficient test for adolescents with SAD as compared to standard paperand-pencil tests. It can be used to diagnose varying degrees of SAD quickly and reliably and ease the burden on adolescents. Potential applications for inexpensive, efficient, and accurate screening of SAD are discussed.

Investigating the Viability of Alternating Model Tree As An Item Selection Algorithm for Constructing Computerized Adaptive Psychological Testing

“…이러한 장점에 기반해 컴퓨터 기반 적응적 검 사는 단축형 검사 개발 방법으로도 활용되고 있으며, 최근 심리검사에 대한 적용이 늘고 있다 (Seo et al, 2019;Walter et al, 2007;Hu et al, 2020).…”