天花板效应
等值
数据收集
心理学
纵向研究
比例(比率)
认知
天花板(云)
可靠性(半导体)
计算机科学
发展心理学
统计
医学
工程类
地理
数学
物理
病理
功率(物理)
神经科学
拉什模型
结构工程
替代医学
量子力学
地图学
作者
Jessica Wise Younger,Kristine D. O’Laughlin,Joaquin A. Anguera,Silvia A. Bunge,Emilio Ferrer,Fumiko Hoeft,Bruce D. McCandliss,Jyoti Mishra,Miriam Rosenberg‐Lee,Adam Gazzaley,Melina R. Uncapher
标识
DOI:10.31234/osf.io/xf489
摘要
This manuscript describes data collection, cleaning, and quality control procedures used in a large-scale, longitudinal, in-school study of executive function skills (EFs) and academic achievement in middle childhood, Project iLEAD (in-school Longitudinal Executive Function and Academic Achievement Database). Assessments were administered in real-world educational settings in a large sample of third through eighth grade students (ages 7 to 14; N = 1,280) over two years, with eight data collection timepoints in group settings. We assessed students with a novel, mobile EF assessment tool Adaptive Cognitive Evaluation (ACE). This battery included 11 tasks that were each designed to adapt to user performance in a trial-wise manner, allowing the same battery of tasks to be used multiple times within the same individual, and across a wide range of ages. Data quality analyses revealed that the adaptive algorithms were successful in equating challenge levels across ages 7 through 14 for 10 of 11 tasks. Data for each task were found to be approximately normally distributed and split-half reliability was acceptable across both accuracy and reaction time. ACE thus provides a reliable way to assess EFs in middle childhood using the same tasks while maintaining appropriate challenge level without facing ceiling or floor effects.
科研通智能强力驱动
Strongly Powered by AbleSci AI