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Using Binary Paradata to Correct for Measurement Error in Survey Data Analysis

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  • Damião Nóbrega Da Silva
  • Chris Skinner
  • Jae Kwang Kim
Abstract
Paradata refers here to data at unit level on an observed auxiliary variable, not usually of direct scientific interest, which may be informative about the quality of the survey data for the unit. There is increasing interest among survey researchers in how to use such data. Its use to reduce bias from nonresponse has received more attention so far than its use to correct for measurement error. This article considers the latter with a focus on binary paradata indicating the presence of measurement error. A motivating application concerns inference about a regression model, where earnings is a covariate measured with error and whether a respondent refers to pay records is the paradata variable. We specify a parametric model allowing for either normally or t-distributed measurement errors and discuss the assumptions required to identify the regression coefficients. We propose two estimation approaches that take account of complex survey designs: pseudo-maximum likelihood estimation and parametric fractional imputation. These approaches are assessed in a simulation study and are applied to a regression of a measure of deprivation given earnings and other covariates using British Household Panel Survey data. It is found that the proposed approach to correcting for measurement error reduces bias and improves on the precision of a simple approach based on accurate observations. We outline briefly possible extensions to uses of this approach at earlier stages in the survey process. Supplemental materials are available online.

Suggested Citation

  • Damião Nóbrega Da Silva & Chris Skinner & Jae Kwang Kim, 2016. "Using Binary Paradata to Correct for Measurement Error in Survey Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 526-537, April.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:514:p:526-537
    DOI: 10.1080/01621459.2015.1130632
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    References listed on IDEAS

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    1. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
    2. Jae Kwang Kim, 2011. "Parametric fractional imputation for missing data analysis," Biometrika, Biometrika Trust, vol. 98(1), pages 119-132.
    3. Yulei He & Alan M. Zaslavsky, 2009. "Combining Information from Cancer Registry and Medical Records Data to Improve Analyses of Adjuvant Cancer Therapies," Biometrics, The International Biometric Society, vol. 65(3), pages 946-952, September.
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    Cited by:

    1. Heng Chen & Geoffrey Dunbar & Q. Rallye Shen, 2020. "The Mode is the Message: Using Predata as Exclusion Restrictions to Evaluate Survey Design," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 341-357, Emerald Group Publishing Limited.
    2. Mengli Zhang & Yang Bai, 2021. "On the use of repeated measurement errors in linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 779-803, July.
    3. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    4. Jae Kwang Kim & J.N.K. Rao & Yonghyun Kwon, 2022. "Analysis of clustered survey data based on two‐stage informative sampling and associated two‐level models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1522-1540, October.
    5. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.

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