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首页> 外文期刊>Journal of abnormal child psychology >The dimensional nature of externalizing behaviors in adolescence: Evidence from a direct comparison of categorical, dimensional, and hybrid models
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The dimensional nature of externalizing behaviors in adolescence: Evidence from a direct comparison of categorical, dimensional, and hybrid models

机译:青春期外部化行为的维度性质:来自分类,维度和混合模型的直接比较的证据

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Researchers have recognized the importance of developing an accurate classification system for externalizing disorders, though much of this work has been framed by a priori preferences for categorical vs. dimensional constructs. Newer statistical technologies now allow categorical and dimensional models of psychopathology to be compared empirically. In this study, we directly compared the fit of categorical and dimensional models of externalizing behaviors in a large and representative community sample of adolescents at two time points separated by nearly 2.5 years (N=2027; mean age at Time 1=11.09 years; 50.8% female). Delinquent and aggressive behaviors were assessed with child and parent Child Behavior Checklist reports. Latent trait, latent class, and factor mixture models were fit to the data, and at both time points, the latent trait model provided the best fit to the data. The item parameters were inspected and interpreted, and it was determined that the items were differentially sensitive across all regions of the dimension. We conclude that classification models can be based on empirical evidence rather than a priori preferences, and while current classification systems conceptualize externalizing problems in terms of discrete groups, they can be better conceptualized as dimensions.
机译:研究人员已经认识到开发一种精确的分类系统以将疾病外化的重要性,尽管这项工作的大部分内容是根据先验偏爱分类和维构式进行的。现在,较新的统计技术允许根据经验比较心理病理学的分类和维度模型。在这项研究中,我们直接比较了一个大型且有代表性的青少年群体在两个接近2.5年的时间点(N = 2027;时间1-11.09岁的平均年龄; 50.8)的外部化行为的分类和维度模型的拟合%女性)。通过儿童和父母的儿童行为清单报告评估了违法行为和攻击行为。潜在特征,潜在类别和因子混合模型都适合数据,并且在两个时间点,潜在特征模型都提供了最适合数据的模型。检查并解释了项目参数,并确定了项目在维度的所有区域之间都具有不同的敏感性。我们得出的结论是,分类模型可以基于经验证据,而不是先验偏好,并且尽管当前的分类系统从离散组的角度对外部化问题进行概念化,但可以更好地将其作为维度进行概念化。

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