在青少年流行病学研究中评估自我报告的诚实值:比较监督机器学习和推断统计技术
Janaka V Kosgolla1, Douglas C Smith2, Shahana Begum2
1School of Social Work, University of Illinois Urbana-Champaign, 1010 W. Nevada St, Urbana, IL, 61801, USA. janakak2@illinois.edu.
BMC medical research methodology
|September 22, 2023
概括
这项研究证实了青少年吸毒调查的自我报告诚实项. 调查结果显示,该项目可靠地识别出诚实的反应,提高了流行病学研究中的数据准确性.
科学领域:
- 流行病学 流行病学
- 青少年健康 青少年健康
- 调查方法 调查方法
背景情况:
- 自我报告调查对于青少年物质使用数据至关重要,但面临可信性问题.
- 直接通过询问参与者的诚实性来评估数据质量是一个未被充分利用的方法.
研究的目的:
- 在流行病学调查中评估自我报告诚实项目的准确性.
- 为了提高数据质量和点估计,为这个诚实项目建立一个最佳值.
主要方法:
- 利用了2020年伊利诺伊州青年调查的数据,这是一项基于学校的自我报告研究.
- 雇员监督机器学习 (随机森林) 和后勤回归以分析基于诚实项答案的数据子集.
- 研究了诚实值,假药使用,社会可取性和缺失数据之间的关系.
主要成果:
- 在不同的分析中证实了诚实项目及其值的适当性和可靠性.
- 发现较低的诚实得分与报告的假药使用,增加失踪率和社会可取性偏见之间存在相关性.
- 在机器学习和后勤回归分析之间,诚实度值一致.
结论:
- 诚实项目及其值可靠地评估青少年流行病学研究数据质量.
- 研究人员应该纳入自我报告诚实项目,以建模准确的点估计,并解决可疑的报告.
- 缺少数据分析进一步验证了诚实值,加强了该研究的发现和方法.
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