一种用户驱动的方法,用于使用研究产品来经验性地评估国家调查中的项目重要性
Ai Rene Ong1, Robert Schultz1, Sofi Sinozich2
1Institute for Social Research, University of Michigan, 426 Thompson St, Ann Arbor, MI 48104.
概括
减少调查长度可以提高数据质量和受访者体验. 本研究介绍了一种识别和删除不重要的调查项目的方法,成功地从博士学位受取者调查中删除了17%的项目.
科学领域:
- 调查方法调查方法.
- 数据科学是数据科学.
- 社会科学研究社会科学研究
背景情况:
- 大规模的国家调查是必不可少的,但对于参与者来说往往是漫长而繁的.
- 减少调查长度可以提高数据质量和受访者参与度.
- 删除项目需要仔细的经验评估,以平衡负担和数据实用性.
研究的目的:
- 提出和展示一种方法,以实证地评估调查项目的重要性和受访者负担.
- 引导国家调查项目减少的决策过程.
- 从博士学位获得者调查 (SDR) 中识别和删除非必不可少的项目.
主要方法:
- 采用混合方法方法,结合了文献计量分析,网站数据和paradata.
- 编码了使用SDR数据对变量使用和引用数量的文档文献.
- 分析了SDR网站数据以获取总结统计数据和网络时间数据,包括断裂率.
主要成果:
- 确定了35个未使用的SDR项目,占调查工具的17%.
- 发现物品负担和物品重要性之间存在正相关性,这表明最负担的物品往往具有高度价值.
- 证明了经验性评估项目重要性和负担的可行性.
结论:
- 拟议的方法有效地识别了非必要的调查项目,减少了受访者负担.
- 优先删除未使用的项目可以简化数据收集,而不会损害重要的研究成果.
- 这些发现为优化国家调查设计和受访者体验提供了可扩展的方法.
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