在比较嵌入式自适应干预措施时,对统计效率的方法在一个 SMART 中
Timothy Lycurgus1, Amy Kilbourne2, Daniel Almirall1
1University of Michigan.
概括
本研究介绍了教育适应性干预的顺序性多重分配随机试验 (SMARTs). 它介绍了四种统计技术,以提高分析SMART数据的效率,这对于教育研究中的小效果尺寸至关重要.
科学领域:
- 教育科学教育科学教育
- 行为科学 行为科学
- 生物统计学 生物统计学
背景情况:
- 适应性干预反映了教育学习的顺序性和量身定制性.
- 顺序,多重分配随机试验 (SMART) 越来越多地用于优化这些干预措施.
- 在教育研究中观察到的小效应大小需要对SMART进行统计学上高效的分析方法.
研究的目的:
- 为教育研究人员提供适应性干预和SMART设计的概述.
- 提出四种创新技术,以提高SMART分析中的统计效率.
- 通过现实世界SMART和模拟研究来证明这些技术的实际好处.
主要方法:
- 适应性干预和SMART设计原则的概述.
- 建议和描述四种统计技术,以提高分析效率.
- 将技术应用于SMART,优化学校的认知行为疗法.
- 综合模拟研究,以验证技术的有效性.
主要成果:
- 提出的技术显示了在SMART分析中提高统计效率的潜力.
- 对现实世界SMART和模拟结果的分析证实了这些技术的好处.
- 这些技术很容易使用标准的统计软件或提供的R代码来实现.
结论:
- 适应性干预和SMART是教育科学中宝贵的工具.
- 统计效率对于分析基于教育的SMART是至关重要的,因为效果大小通常很小.
- 提出的技术为提高教育研究中SMART分析的统计能力和效率提供了实际解决方案.
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