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A Unified Approach to Measurement Error and Missing Data: Overview and Applications

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  • Matthew Blackwell
  • James Honaker
  • Gary King

Abstract

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extensions (Blackwell, Honaker, and King, 2014b).

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  • Matthew Blackwell & James Honaker & Gary King, "undated". "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Working Paper 6388, Harvard University OpenScholar.
    • Handle: RePEc:qsh:wpaper:6388
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    File URL: http://gking.harvard.edu//node/6388
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    1. Jonathan N. Katz & Gabriel Katz, 2010. "Correcting for Survey Misreports Using Auxiliary Information with an Application to Estimating Turnout," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 815-835, July.
    2. Brownstone, David & Valletta, Robert G, 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 705-717, November.
    3. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Details and Extensions," Sociological Methods & Research, , vol. 46(3), pages 342-369, August.
    4. Klepper, Steven & Leamer, Edward E, 1984. "Consistent Sets of Estimates for Regressions with Errors in All Variables," Econometrica, Econometric Society, vol. 52(1), pages 163-183, January.
    5. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    6. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Sociological Methods & Research, , vol. 46(3), pages 303-341, August.
    7. Kosuke Imai & Teppei Yamamoto, 2010. "Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 543-560, April.
    8. Ansolabehere, Stephen & Rodden, Jonathan & Snyder, James M., 2008. "The Strength of Issues: Using Multiple Measures to Gauge Preference Stability, Ideological Constraint, and Issue Voting," American Political Science Review, Cambridge University Press, vol. 102(2), pages 215-232, May.
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