一种数据驱动的方法来理解非响应和恢复英国Next Steps队列中的样本代表性
Richard J Silverwood1, Lisa Calderwood1, Morag Henderson1
1University College London, UK.
概括
在纵向调查中确定非响应的预测因素至关重要,比如英国的Next Steps研究. 将这些预测因素纳入统计方法,例如多重归算,可以帮助减少偏见并提高样本的代表性.
科学领域:
- 社会科学 社会科学 社会科学
- 统计 统计 统计 统计
- 流行病学 流行病学
背景情况:
- 纵向调查中的非响应是一个重大挑战,可能会损害研究效率并引入偏见.
- 解决缺失数据需要有原则的统计方法,例如多重归算,特别是当数据不完全随机缺失时.
- 在归算模型中将非响应预测因子作为辅助变量可以加强缺失的随机假设,从而减轻偏差.
研究的目的:
- 在"下一步"英国国家队列研究中,系统地确定第8波 (25-26岁) 没有反应的预测因素.
- 评估已识别的非响应预测因子在使用多重归算恢复样本代表性的有用性.
主要方法:
- 采用数据驱动方法来确定下一步研究样本 (n=15,770) 中的非响应预测因素.
- 预测因素被分类为个人特征,学历,行为,心理健康,社会经济地位和接触/完成实用性.
- 进行了多次归算分析,将确定的预测因素作为辅助变量.
主要成果:
- 在多个领域中确定了一套全面的非响应预测指标.
- 将这些预测因子作为多重归算中的辅助变量成功地恢复了各种分析环境中的样本代表性.
- 虽然在本研究中有效,但这些预测因素对偏差减少的普遍适用性被认为是潜在的有限.
结论:
- 已识别的非响应预测因子被推用于未来对Next Steps研究的分析,以减轻偏差.
- 拟议的数据驱动方法作为调查和减少其他纵向研究中非响应偏差的模型.
- 使用具有识别预测因子的原则方法是保持纵向调查数据完整性和代表性的关键.
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