IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v72y2010i1p27-48.html
   My bibliography  Save this article

Combining information from multiple surveys by using regression for efficient small domain estimation

Author

Listed:
  • Takis Merkouris

Abstract

Summary. In sample surveys of finite populations, subpopulations for which the sample size is too small for estimation of adequate precision are referred to as small domains. Demand for small domain estimates has been growing in recent years among users of survey data. We explore the possibility of enhancing the precision of domain estimators by combining comparable information collected in multiple surveys of the same population. For this, we propose a regression method of estimation that is essentially an extended calibration procedure whereby comparable domain estimates from the various surveys are calibrated to each other. We show through analytic results and an empirical study that this method may greatly improve the precision of domain estimators for the variables that are common to these surveys, as these estimators make effective use of increased sample size for the common survey items. The design‐based direct estimators proposed involve only domain‐specific data on the variables of interest. This is in contrast with small domain (mostly small area) indirect estimators, based on a single survey, which incorporate through modelling data that are external to the targeted small domains. The approach proposed is also highly effective in handling the closely related problem of estimation for rare population characteristics.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssb:v:72:y:2010:i:1:p:27-48
    DOI: 10.1111/j.1467-9868.2009.00724.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2009.00724.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2009.00724.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Michael R. Elliott & William W. Davis, 2005. "Corrigendum: Obtaining cancer risk factor prevalence estimates in small areas: combining data from two surveys," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 958-958, November.
    3. Raghunathan, Trivellore E. & Xie, Dawei & Schenker, Nathaniel & Parsons, Van L. & Davis, William W. & Dodd, Kevin W. & Feuer, Eric J., 2007. "Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 474-486, June.
    4. Michael R. Elliott & William W. Davis, 2005. "Obtaining cancer risk factor prevalence estimates in small areas: combining data from two surveys," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 595-609, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jae Kwang Kim & Zhonglei Wang & Zhengyuan Zhu & Nathan B. Cruze, 2018. "Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 175-189, June.
    2. Alessio Guandalini & Yves Tillé, 2017. "Design-based Estimators Calibrated on Estimated Totals from Multiple Surveys," International Statistical Review, International Statistical Institute, vol. 85(2), pages 250-269, August.
    3. 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.
    4. Yves G. Berger & Ewa Kabzińska, 2020. "Empirical Likelihood Approach for Aligning Information from Multiple Surveys," International Statistical Review, International Statistical Institute, vol. 88(1), pages 54-74, April.
    5. Paolo Righi, 2016. "Estimation procedure and inference for component totals of the economic aggregates in the “Frame SBS”," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 18(1), pages 83-97.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jae Kwang Kim & Zhonglei Wang & Zhengyuan Zhu & Nathan B. Cruze, 2018. "Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 175-189, June.
    2. Giancarlo Manzi & David J. Spiegelhalter & Rebecca M. Turner & Julian Flowers & Simon G. Thompson, 2011. "Modelling bias in combining small area prevalence estimates from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 31-50, January.
    3. Marissa B. Reitsma & Sherri Rose & Alex Reinhart & Jeremy D. Goldhaber-Fiebert & Joshua A. Salomon, 2024. "Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey," Medical Decision Making, , vol. 44(2), pages 175-188, February.
    4. Rasner, Anika & Frick, Joachim R. & Grabka, Markus M., 2013. "Statistical Matching of Administrative and Survey Data: An Application to Wealth Inequality Analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 42(2), pages 192-224.
    5. Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2013. "Statistical Matching of Administrative and Survey Data," Sociological Methods & Research, , vol. 42(2), pages 192-224, May.
    6. Frauke Kreuter, 2013. "Facing the Nonresponse Challenge," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 23-35, January.
    7. Chipperfield James O., 2016. "Discussion," Journal of Official Statistics, Sciendo, vol. 32(2), pages 287-289, June.
    8. Jan Pablo Burgard & Ralf Münnich & Martin Rupp, 2019. "A Generalized Calibration Approach Ensuring Coherent Estimates with Small Area Constraints," Research Papers in Economics 2019-10, University of Trier, Department of Economics.
    9. Anne Konrad & Jan Pablo Burgard & Ralf Münnich, 2021. "A Two‐level GREG Estimator for Consistent Estimation in Household Surveys," International Statistical Review, International Statistical Institute, vol. 89(3), pages 635-656, December.
    10. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    11. Andrew Lawson & Anna Schritz & Luis Villarroel & Gloria A. Aguayo, 2020. "Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example," IJERPH, MDPI, vol. 17(5), pages 1-20, March.
    12. Denis Heng Yan Leung & Ken Yamada & Biao Zhang, 2015. "Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 155-179, March.
    13. Christopher S. Withers & Saralees Nadarajah, 2016. "M-Estimators for Regression with Changing Scale," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(2), pages 238-286, November.
    14. Yves G. Berger & Ewa Kabzińska, 2020. "Empirical Likelihood Approach for Aligning Information from Multiple Surveys," International Statistical Review, International Statistical Institute, vol. 88(1), pages 54-74, April.
    15. 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.
    16. Alessio Guandalini & Yves Tillé, 2017. "Design-based Estimators Calibrated on Estimated Totals from Multiple Surveys," International Statistical Review, International Statistical Institute, vol. 85(2), pages 250-269, August.
    17. Schmid, Timo & Bruckschen, Fabian & Salvati, Nicola & Zbiranski, Till, 2016. "Constructing socio-demographic indicators for National Statistical Institutes using mobile phone data: Estimating literacy rates in Senegal," Discussion Papers 2016/9, Free University Berlin, School of Business & Economics.
    18. Sheyla Rodrigues Cassy & Samuel Manda & Filipe Marques & Maria do Rosário Oliveira Martins, 2022. "Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique," IJERPH, MDPI, vol. 19(10), pages 1-15, May.
    19. Kevin Watjou & Christel Faes & Yannick Vandendijck, 2020. "Spatial Modelling to Inform Public Health Based on Health Surveys: Impact of Unsampled Areas at Lower Geographical Scale," IJERPH, MDPI, vol. 17(3), pages 1-19, January.
    20. Paolo Righi, 2016. "Estimation procedure and inference for component totals of the economic aggregates in the “Frame SBS”," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 18(1), pages 83-97.
    21. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssb:v:72:y:2010:i:1:p:27-48. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.