Ahmad Amar S, Al-Hassan Munther, Hussain Hamid Y, Juber Nirmin F, Kiwanuka Fred N, Hag-Ali Mohammed, Ali Raghib
New York University, Abu Dhabi, UAE.
Higher Colleges of Technology, Dubai Men's College, Abu Dhabi, UAE.
Stat Pap (Berl). 2023 Feb 21:1-18. doi: 10.1007/s00362-023-01405-4.
When self-reported data are used in statistical analysis to estimate the mean and variance, as well as the regression parameters, the estimates tend, in many cases, to be biased. This is because interviewees have a tendency to heap their answers to certain values. The aim of the paper is to examine the bias-inducing effect of the heaping error in self-reported data, and study the effect on the heaping error on the mean and variance of a distribution as well as the regression parameters. As a result a new method is introduced to correct the effects of bias due to the heaping error using validation data. Using publicly available data and simulation studies, it can be shown that the newly developed method is practical and can easily be applied to correct the bias in the estimated mean and variance, as well as in the estimated regression parameters computed from self-reported data. Hence, using the method of correction presented in this paper allows researchers to draw accurate conclusions leading to the right decisions, e.g. regarding health care planning and delivery.
当在统计分析中使用自我报告数据来估计均值、方差以及回归参数时，在许多情况下，这些估计往往会产生偏差。这是因为受访者倾向于将他们的答案集中在某些值上。本文的目的是研究自我报告数据中堆积误差的偏差诱导效应，并研究堆积误差对分布的均值和方差以及回归参数的影响。结果引入了一种新方法，利用验证数据来校正由于堆积误差导致的偏差效应。通过使用公开可用的数据和模拟研究，可以表明新开发的方法是实用的，并且可以很容易地应用于校正从自我报告数据计算出的估计均值和方差以及估计回归参数中的偏差。因此，使用本文提出的校正方法可以使研究人员得出准确的结论，从而做出正确的决策，例如在医疗保健规划和提供方面。
