IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v185y2022i4p1522-1540.html
   My bibliography  Save this article

Analysis of clustered survey data based on two‐stage informative sampling and associated two‐level models

Author

Listed:
  • Jae Kwang Kim
  • J.N.K. Rao
  • Yonghyun Kwon

Abstract

This paper deals with making inference on parameters of a two‐level model matching the design hierarchy of a two‐stage sample. In a pioneering paper, Scott and Smith (Journal of the American Statistical Association, 1969, 64, 830–840) proposed a Bayesian model based or prediction approach to estimating a finite population mean under two‐stage cluster sampling. We provide a brief account of their pioneering work. We review two methods for the analysis of two‐level models based on matching two‐stage samples. Those methods are based on pseudo maximum likelihood and pseudo composite likelihood taking account of design weights. We then propose a new method for analysis of two‐level models based on a normal approximation to the estimated cluster effects and taking account of design weights. This method does not require cluster sizes to be constants or unrelated to cluster effects. We evaluate the relative performance of the three methods in a simulation study. Finally, we apply the methods to real data obtained from 2011 Nepal Demographic and Health Survey (NDHS).

Suggested Citation

  • Jae Kwang Kim & J.N.K. Rao & Yonghyun Kwon, 2022. "Analysis of clustered survey data based on two‐stage informative sampling and associated two‐level models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1522-1540, October.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:4:p:1522-1540
    DOI: 10.1111/rssa.12805
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12805
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12805?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. D Lee & J K Kim & C J Skinner, 2019. "Within-cluster resampling for multilevel models under informative cluster size," Biometrika, Biometrika Trust, vol. 106(4), pages 965-972.
    2. Da Silva, Damião Nóbrega & Skinner, Chris J. & Kim, Jae Kwang, 2016. "Using binary paradata to correct for measurement error in survey data analysis," LSE Research Online Documents on Economics 64763, London School of Economics and Political Science, LSE Library.
    3. Jae Kwang Kim & C. J. Skinner, 2013. "Weighting in survey analysis under informative sampling," Biometrika, Biometrika Trust, vol. 100(2), pages 385-398.
    4. E. Benhin & J. N. K. Rao & A. J. Scott, 2005. "Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes," Biometrika, Biometrika Trust, vol. 92(2), pages 435-450, June.
    5. Laura Dumitrescu & Wei Qian & J. N. K. Rao, 2021. "A Weighted Composite Likelihood Approach to Inference from Clustered Survey Data Under a Two-Level Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 814-843, August.
    6. Damião Nóbrega Da Silva & Chris Skinner & Jae Kwang Kim, 2016. "Using Binary Paradata to Correct for Measurement Error in Survey Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 526-537, April.
    7. D. Pfeffermann & C. J. Skinner & D. J. Holmes & H. Goldstein & J. Rasbash, 1998. "Weighting for unequal selection probabilities in multilevel models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 23-40.
    Full references (including those not matched with items on IDEAS)

    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. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    2. Mengli Zhang & Yang Bai, 2021. "On the use of repeated measurement errors in linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 779-803, July.
    3. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.
    4. Robert G. Clark & David G. Steel, 2022. "Sample design for analysis using high‐influence probability sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1733-1756, October.
    5. Corder Nathan & Yang Shu, 2020. "Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 249-271, January.
    6. Maciej Beręsewicz & Dagmara Nikulin, 2018. "Informal employment in Poland: an empirical spatial analysis," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(3), pages 338-355, July.
    7. Bowen, Mary Elizabeth, 2009. "Childhood socioeconomic status and racial differences in disability: Evidence from the Health and Retirement Study (1998-2006)," Social Science & Medicine, Elsevier, vol. 69(3), pages 433-441, August.
    8. Peter Z. Schochet, 2013. "Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 219-238, June.
    9. Dunstan Matekenya & Francis Mulangu & David Newhouse, 2025. "Malnourished but Not Destitute: The Spatial Interplay Between Nutrition and Poverty in Madagascar," Journal of International Development, John Wiley & Sons, Ltd., vol. 37(2), pages 554-569, March.
    10. Ana Maria Osorio & Catalina Bolancé & Nyovane Madise & Katharina Rathmann, 2013. "Social Determinants of Child Health in Colombia: Can Community Education Moderate the Effect of Family Characteristics?," Working Papers XREAP2013-02, Xarxa de Referència en Economia Aplicada (XREAP), revised Mar 2013.
    11. Jaakko Nevalainen & Somnath Datta & Hannu Oja, 2014. "Inference on the marginal distribution of clustered data with informative cluster size," Statistical Papers, Springer, vol. 55(1), pages 71-92, February.
    12. Federico Bugni & Ivan A. Canay & Azeem M. Shaikh & Max Tabord-Meehan, 2025. "Inference for Cluster Randomized Experiments with Nonignorable Cluster Sizes," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 3(2), pages 255-288.
    13. Hammon, Angelina & Zinn, Sabine, 2020. "Multiple imputation of binary multilevel missing not at random data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 69(3), pages 547-564.
    14. Manuel Coutinho Pereira, 2010. "Educational Attainment and Equality of Opportunity in Portugal and in Europe: The Role of School Versus Parental Influence," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    15. Jungah Choi & Hyunsuk Han, 2023. "Understanding the Influence of Teacher-Student Relationship on Mathematics Achievement: Evidence From Korean Students," SAGE Open, , vol. 13(4), pages 21582440231, November.
    16. Amini, Chiara & Nivorozhkin, Eugene, 2015. "The urban–rural divide in educational outcomes: Evidence from Russia," International Journal of Educational Development, Elsevier, vol. 44(C), pages 118-133.
    17. Ana Maria Osorio & Catalina Bolancé & Nyovani Madise, 2012. "Intermediary and structural determinants of early childhood health in Colombia: exploring the role of communities," Working Papers XREAP2012-13, Xarxa de Referència en Economia Aplicada (XREAP), revised Jun 2012.
    18. Isabella Corazziari & Gabriele Ascari & Maria Giuseppina Muratore, 2024. "If the tools to gather information affect data quality: violence against women survey case," METRON, Springer;Sapienza Università di Roma, vol. 82(1), pages 37-70, April.
    19. Woodward, Albert & Das, Abhik & Raskin, Ira E. & Morgan-Lopez, Antonio A., 2006. "An exploratory analysis of treatment completion and client and organizational factors using hierarchical linear modeling," Evaluation and Program Planning, Elsevier, vol. 29(4), pages 335-351, November.
    20. repec:plo:pone00:0210405 is not listed on IDEAS
    21. Woojin Chung & Roeul Kim, 2020. "A Reversal of the Association between Education Level and Obesity Risk during Ageing: A Gender-Specific Longitudinal Study in South Korea," IJERPH, MDPI, vol. 17(18), pages 1-19, September.

    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:jorssa:v:185:y:2022:i:4:p:1522-1540. 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.