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Calibrating non‐probability surveys to estimated control totals using LASSO, with an application to political polling

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  • Jack Kuang Tsung Chen
  • Richard L. Valliant
  • Michael R. Elliott

Abstract

Declining response rates and increasing costs have led to greater use of non‐probability samples in election polling. But non‐probability samples may suffer from selection bias due to differential access, degrees of interest and other factors. Here we estimate voting preference for 19 elections in the US 2014 midterm elections by using large non‐probability surveys obtained from SurveyMonkey users, calibrated to estimated control totals using model‐assisted calibration combined with adaptive LASSO regression, or the estimated controlled LASSO, ECLASSO. Comparing the bias and root‐mean‐square error of ECLASSO with traditional calibration methods shows that ECLASSO can be a powerful method for adjusting non‐probability surveys even when only a small sample is available from a probability survey. The methodology proposed has potentially broad application across social science and health research, as response rates for probability samples decline and access to non‐probability samples increases.

Suggested Citation

  • Jack Kuang Tsung Chen & Richard L. Valliant & Michael R. Elliott, 2019. "Calibrating non‐probability surveys to estimated control totals using LASSO, with an application to political polling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 657-681, April.
    • Handle: RePEc:bla:jorssc:v:68:y:2019:i:3:p:657-681
    DOI: 10.1111/rssc.12327
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    Cited by:

    1. Maria Börjesson & Marco Kouwenhoven & Gerard Jong & Andrew Daly, 2023. "Can repeated surveys reveal the variation of the value of travel time over time?," Transportation, Springer, vol. 50(1), pages 245-284, February.
    2. Luis Castro-Martín & Maria del Mar Rueda & Ramón Ferri-García, 2020. "Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    3. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    4. Maciej Berk{e}sewicz & Greta Bia{l}kowska & Krzysztof Marcinkowski & Magdalena Ma'slak & Piotr Opiela & Robert Pater & Katarzyna Zadroga, 2019. "Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration," Papers 1908.06731, arXiv.org.
    5. Luis Castro-Martín & María del Mar Rueda & Ramón Ferri-García, 2020. "Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment," Mathematics, MDPI, vol. 8(11), pages 1-14, November.
    6. Yingli Pan & Wen Cai & Zhan Liu, 2022. "Inference for non-probability samples under high-dimensional covariate-adjusted superpopulation model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 955-979, October.
    7. Élisabeth Tovar & Matthieu Bunel, 2021. "Attitudes on past-in-present educational discrimination. Insights from a representative factorial survey," EconomiX Working Papers 2021-28, University of Paris Nanterre, EconomiX.

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