Cargando…

Psychometric Properties of the SAS, BAI, and S-AI in Chinese University Students

Three widely-used self-report anxiety scales, including the Self-Rating Anxiety Scale (SAS), the Beck Anxiety Inventory (BAI), and the State Anxiety Inventory (S-AI), were used to simultaneously compare the psychometric properties via an item response theory (IRT) model with Chinese university stude...

Descripción completa

Detalles Bibliográficos
Autores principales: Pang, Zhanyu, Tu, Dongbo, Cai, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365890/
https://www.ncbi.nlm.nih.gov/pubmed/30766501
http://dx.doi.org/10.3389/fpsyg.2019.00093
Descripción
Sumario:Three widely-used self-report anxiety scales, including the Self-Rating Anxiety Scale (SAS), the Beck Anxiety Inventory (BAI), and the State Anxiety Inventory (S-AI), were used to simultaneously compare the psychometric properties via an item response theory (IRT) model with Chinese university students as the sample. Although these scales were probably to measure the same underlying construct, namely, anxiety, their psychometric properties were different. Results showed that the BAI’s measurement error was fewer than that of the other scales, with their anxiety severity ranging approximately from the 0.8 standard deviations below the mean to 3 standard deviations above the mean, while the S-AI’s measurement error was fewer than that of the other degrees of anxiety. The S-AI provided more information than the other scales when the student’s scale was less than approximately 0.8 standard deviations below the mean of anxiety severity. In general, the BAI showed better, for it provided more information than the other scales at the broadest range of anxiety severity. The SAS provided less information than the other scales at all anxiety severity range. In conclusion, BAI shows good psychometric quality. Finally, the three instruments were combined on a common scale by using IRT model and a conversion table was provided so as to achieve the transformation of each scale score.