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相关概念视频

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings.

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相关实验视频

Updated: Jul 20, 2026

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
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通过使用线性方程,内核方程,项目响应理论和机器学习方法协调CBCL和SDQADHD分数.

Miljan Jović1, Maryam Amir Haeri1, Andrew Whitehouse2

  • 1Department of Learning, Data Analytics and Technology, University of Twente, Enschede, Netherlands.

Frontiers in psychology
|July 25, 2024
PubMed
概括

从儿童行为检查清单 (CBCL) 和优点和困难问卷 (SDQ) 中协调ADHD分数对于研究至关重要. 使用项目级数据的回归方法在规模协调方面被证明最有效.

关键词:
更多关于 ADHD ADHD 的文章在此之前,IRT是IRT.数据统一和数据协调.核心等同化等同化线性等价法是指线性等价法.机器学习是机器学习.测试等同化测试等同化测试

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科学领域:

  • 心理测量 心理测量 心理测量
  • 发展心理学 发展心理学
  • 临床心理学 临床心理学

背景情况:

  • 研究人员经常面临比较结果的挑战,原因是机构使用的各种尺度.
  • 在研究联盟中,协调尺度对于有意义的数据聚合和分数比较至关重要.

研究的目的:

  • 协调儿童行为检查表 (CBCL) 和优点和困难问卷 (SDQ) 的注意力缺陷/多动症 (ADHD) 评分.
  • 评估和比较各种测试等效方法的有效性,以实现尺度协调.

主要方法:

  • 采用普通人设计,对10至11.5岁儿童的1551份家长报告.
  • 应用了线性方程,内核方程,物件响应理论 (IRT) 和机器学习方法 (回归,随机森林,支持矢量机器).
  • 在交叉验证中使用根平均平方误差 (RMSE) 评估方法的有效性.

主要成果:

  • 回归方法,特别是那些将结果视为间隔测量并使用项目级信息的方法,表现出卓越的性能.
  • 机器学习方法,包括回归,在有效协调ADHD得分方面显示出希望.

结论:

  • 在单组设计中,建议使用回归方法来协调CBCL和SDQ的ADHD得分.
  • 项目级数据和间隔测量假设提高了ADHD构造的尺度协调的准确性.