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Estimation of mask effectiveness perception for small domains using multiple data sources

Author

Listed:
  • Sen Aditi

    (PhD Student, Applied Mathematics & Statistics, and Scientific Computation, .)

  • Lahiri Partha

    (Director and Professor, The Joint Program in Survey Methodology & Professor, Department of Mathematics, University of Maryland, College Park, MD 20742, USA, .)

Abstract

Understanding the impacts of pandemics on public health and related societal issues at granular levels is of great interest. COVID-19 is affecting everyone in the globe and mask wearing is one of the few precautions against it. To quantify people’s perception of mask effectiveness and to prevent the spread of COVID-19 for small areas, we use Understanding America Study’s (UAS) survey data on COVID-19 as our primary data source. Our data analysis shows that direct survey-weighted estimates for small areas could be highly unreliable. In this paper, we develop a synthetic estimation method to estimate proportions of perceived mask effectiveness for small areas using a logistic model that combines information from multiple data sources. We select our working model using an extensive data analysis facilitated by a new variable selection criterion for survey data and benchmarking ratios. We suggest a jackknife method to estimate the variance of our estimator. From our data analysis, it is evident that our proposed synthetic method outperforms the direct survey-weighted estimator with respect to commonly used evaluation measures.

Suggested Citation

  • Sen Aditi & Lahiri Partha, 2022. "Estimation of mask effectiveness perception for small domains using multiple data sources," Statistics in Transition New Series, Statistics Poland, vol. 23(1), pages 1-20, March.
    • Handle: RePEc:vrs:stintr:v:23:y:2022:i:1:p:1-20:n:2
    DOI: 10.2478/stattrans-2022-0001
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