Testing for measurement error in survey data analysis using paradata

D N Da Silva and C J Skinner

Biometrika, 2021, vol. 108, issue 1, 239-246

Abstract: SummaryParadata 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.

Keywords: Complex survey; Errors-in-variables regression; Instrumental variable; Quasi-score test (search for similar items in EconPapers)
Date: 2021
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