教程:评估非可忽视性缺失对回归分析的影响,使用本地对非可忽视性敏感度指数
Bocheng Jing1, Yi Qian2, Daniel F Heitjan3
1Faculty of Health Sciences, Simon Fraser University.
Psychological methods
|November 16, 2023
概括
心理学研究经常面临缺失的数据. 本研究引入了对不可忽视性的局部敏感性指数 (ISNI),用于在数据不随机丢失时 (MAR) 进行可靠的分析,确保可靠的发现.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
背景情况:
- 缺失数据在心理学研究中很普遍,往往需要随机缺失数据 (MAR) 的假设.
- 马尔假设是不可验证的,错误的应用可能会在统计分析中引入偏见.
- 评估调查结果对潜在违反MAR假设的可靠性对于可信的研究至关重要.
研究的目的:
- 引入一种新的敏感性分析类别,用于评估偏离MAR假设的影响.
- 将本地对不可忽视性的敏感性指数 (ISNI) 作为强度的实际衡量标准.
- 为在回归模型中实施这些灵敏度分析提供一个可访问的R包 (isni).
主要方法:
- 这项研究的灵敏度分析来源于对不可忽视性的局部灵敏度指数 (ISNI).
- ISNI提供了一个计算上简单的方法,避免了复杂的非MAR缺失数据模型.
- 该方法在R包"isni"中实现,用于各种回归模型.
主要成果:
- 拟议的方法ISNI提供了一个可计算的测量方法,用于评估结论对非MAR数据的敏感性.
- "isni" R套件简化了这些灵敏度分析的应用.
- 对现实世界心理学数据集的说明性分析表明了该方法的实际实用性.
结论:
- ISNI方法为心理学家提供了一种有价值的工具,以进行敏感性分析,并在潜在的MAR违规的情况下评估其发现的可信性.
- 附带的R包使研究人员能够获得先进的缺失数据敏感性分析.
- 这种方法提高了使用统计建模的心理学研究的严谨性和可靠性.
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