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A measurement error model approach to survey data integration: combining information from two surveys

Author

Listed:
  • Seho Park

    (Iowa State University)

  • Jae Kwang Kim

    (Iowa State University)

  • Diana Stukel

    (FANTA III Project, FHI 360)

Abstract

Combining information from several surveys from the same target population is an important practical problem in survey sampling. The paper is motivated by work that authors undertook, sponsored by the Food and Nutrition Technical Assistance III Project (FANTA), with funding from the U.S. Agency for International Development (USAID) Bureau of Food Security (BFS). In the project, two surveys were conducted independently for some areas and we present a measurement error model approach to integrate mean estimates obtained from the two surveys. The predicted values for the counterfactual outcome are used to create composite estimates for the overlapped areas. An application of the technique to the project is provided.

Suggested Citation

  • Seho Park & Jae Kwang Kim & Diana Stukel, 2017. "A measurement error model approach to survey data integration: combining information from two surveys," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 345-357, December.
    • Handle: RePEc:spr:metron:v:75:y:2017:i:3:d:10.1007_s40300-017-0124-0
    DOI: 10.1007/s40300-017-0124-0
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    References listed on IDEAS

    as
    1. Jae Kwang Kim & J. N. K. Rao, 2012. "Combining data from two independent surveys: a model-assisted approach," Biometrika, Biometrika Trust, vol. 99(1), pages 85-100.
    2. Takis Merkouris, 2004. "Combining Independent Regression Estimators From Multiple Surveys," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1131-1139, December.
    3. Jae Kwang Kim, 2011. "Parametric fractional imputation for missing data analysis," Biometrika, Biometrika Trust, vol. 98(1), pages 119-132.
    4. Takis Merkouris, 2010. "Combining information from multiple surveys by using regression for efficient small domain estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 27-48, January.
    5. Lynn M. R. Ybarra & Sharon L. Lohr, 2008. "Small area estimation when auxiliary information is measured with error," Biometrika, Biometrika Trust, vol. 95(4), pages 919-931.
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    Cited by:

    1. Jean D. Opsomer & M. Giovanna Ranalli & Maria Michela Dickson, 2017. "Foreword to the special issue on “Advances in Survey Statistics”," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 245-247, December.
    2. Camilla Salvatore, 2023. "Inference with non-probability samples and survey data integration: a science mapping study," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 83-107, April.

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