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Testing for measurement error in survey data analysis using paradata
Biometrika ( IF 2.4 ) Pub Date : 2020-08-04 , DOI: 10.1093/biomet/asaa050
D N Da Silva 1 , C J Skinner 2
Affiliation  

Paradata refers to survey variables which are not of direct interest themselves, but are related to the quality of data on survey variables which are of interest. We focus on a categorical paradata variable, which reflects the presence of measurement error in a variable of interest. We propose a quasi-score test of the hypothesis of no measurement error bias in the estimation of regression coefficients under models for paradata. We also propose a regression-based test, analogous to a simple test proposed by Fuller for instrumental variables. The methods developed can take account of a complex sampling design. In an application with data from the British Household Panel Survey, all tests provide clear evidence of measurement bias in the estimated coefficient of gross pay. In a simulation study, all tests have rejection rates close to the nominal level under the null hypothesis; the quasi-score tests display more power than the regression-based test. The size of the quasi-score test is shown to be robust to some forms of misspecification of the paradata model, both by a theoretical argument and in findings of the simulation study.

中文翻译:

使用Paradata测试调查数据分析中的测量误差

Paradata指的是本身并不是直接关注的调查变量,但与感兴趣的调查变量的数据质量有关。我们关注分类的paradata变量,该变量反映了相关变量中存在测量误差。我们针对准数据模型下的回归系数估计,提出了没有测量误差偏差的假设的准得分检验。我们还提出了一种基于回归的检验,类似于富勒针对工具变量提出的简单检验。开发的方法可以考虑复杂的采样设计。在具有英国家庭面板调查数据的应用程序中,所有测试都提供了明显的证据,表明估计的总工资系数存在计量偏差。在模拟研究中 在原假设下,所有测试的拒绝率均接近标称水平;准得分测试比基于回归的测试显示出更多的功能。无论是从理论上还是从模拟研究的结果来看,准得分测试的大小都对某些形式的paradata模型的错误指定具有鲁棒性。
更新日期:2020-08-04
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