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On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores

Author

Listed:
  • Peter M. Steiner

    (University of Wisconsin-Madison and Northwestern University)

  • Thomas D. Cook

    (Northwestern University)

  • William R. Shadish

    (University of California-Merced)

Abstract

The effect of unreliability of measurement on propensity score (PS) adjusted treatment effects has not been previously studied. The authors report on a study simulating different degrees of unreliability in the multiple covariates that were used to estimate the PS. The simulation uses the same data as two prior studies. Shadish, Clark, and Steiner showed that a PS formed from many covariates demonstrably reduced selection bias, while Steiner, Cook, Shadish, and Clark identified the subsets of covariates from the larger set that were most effective for bias reduction. Adding different degrees of random error to these covariates in a simulation, the authors demonstrate that unreliability of measurement can degrade the ability of PSs to reduce bias. Specifically, increases in reliability only promote bias reduction, if the covariates are effective in reducing bias to begin with. Increasing or decreasing the reliability of covariates that do not effectively reduce selection bias makes no difference at all.

Suggested Citation

  • Peter M. Steiner & Thomas D. Cook & William R. Shadish, 2011. "On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 213-236, April.
    • Handle: RePEc:sae:jedbes:v:36:y:2011:i:2:p:213-236
    DOI: 10.3102/1076998610375835
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    References listed on IDEAS

    as
    1. Shadish, William R. & Clark, M. H. & Steiner, Peter M., 2008. "Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1334-1344.
    2. Cook, Thomas D., 2008. ""Waiting for Life to Arrive": A history of the regression-discontinuity design in Psychology, Statistics and Economics," Journal of Econometrics, Elsevier, vol. 142(2), pages 636-654, February.
    3. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
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