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异常心理学中的多面建构:双因素 S-1 模型对个体临床评估的启示。

Multi-Faceted Constructs in Abnormal Psychology: Implications of the Bifactor S - 1 Model for Individual Clinical Assessment.

机构信息

Department of Psychology, Free University of Berlin(Freie Universität Berlin), Habelschwerdter Allee 45, 14195, Berlin, Germany.

出版信息

J Abnorm Child Psychol. 2020 Jul;48(7):895-900. doi: 10.1007/s10802-020-00624-9.

Abstract

Burns et al. (this issue) have shown that the application of the symmetrical bifactor model to attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) symptoms leads to anomalous and inconsistent results across different rater groups. In contrast to the symmetrical bifactor model, applications of the bifactor S-1 model showed consistent and theoretically well-founded results. The implications of the bifactor S-1 model for individual clinical assessment are discussed. It is shown that individual factor scores of the bifactor S-1 model reveal important information about the profile of individual symptoms that is not captured by factor scores of the multidimensional model with correlated first-order factors. It is argued that for individual clinical assessment factor scores from both types of model (multidimensional model with correlated first-order factors, bifactor S -1 model) should be estimated and compared. Finally, a general strategy for choosing an appropriate model for analyzing multi-faceted constructs is presented that compares areas of applications for (1) the multidimensional model with correlated first-order factors, (2) the bifactor S-1 model with a general reference factor, and (3) the bifactor S - 1 model with a directly assessed general factor.

摘要

伯恩斯等人(本 issue)表明,将对称双因素模型应用于注意力缺陷/多动障碍(ADHD)和对立违抗性障碍(ODD)症状,会导致不同评分者群体得出异常且不一致的结果。与对称双因素模型相反,双因素 S-1 模型的应用得出了一致且具有理论依据的结果。本文讨论了双因素 S-1 模型对个体临床评估的影响。结果表明,双因素 S-1 模型的个体因素得分揭示了有关个体症状特征的重要信息,这些信息无法通过具有相关一阶因素的多维模型的因素得分捕捉到。有人认为,对于个体临床评估,应同时估计和比较两种类型模型(具有相关一阶因素的多维模型、双因素 S-1 模型)的因素得分。最后,提出了一种选择适当模型分析多方面结构的通用策略,该策略比较了(1)具有相关一阶因素的多维模型、(2)具有一般参照因素的双因素 S-1 模型以及(3)具有直接评估的一般因素的双因素 S-1 模型的应用领域。

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