IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v145y2020ics0167947319302610.html
   My bibliography  Save this article

Modelling multilevel data under complex sampling designs: An empirical likelihood approach

Author

Listed:
  • Oǧuz-Alper, Melike
  • Berger, Yves G.

Abstract

Data used in social, behavioural, health or biological sciences may have a hierarchical structure due to the population of interest or the sampling design. Multilevel or marginal models are often used to analyse such hierarchical data. These data are often selected with unequal probabilities from a clustered and stratified population. An empirical likelihood approach for the regression parameters of a multilevel model is proposed. It has the advantage of taking into account of the sampling design. This approach can be used for point estimation, hypothesis testing and confidence intervals for the sub-vector of parameters. It provides asymptotically valid inference for small and large sampling fractions. The simulation study shows the advantages of the empirical likelihood approach over alternative parametric approaches. The approach proposed is illustrated using the Programme for International Student Assessment (PISA) survey data.

Suggested Citation

  • Oǧuz-Alper, Melike & Berger, Yves G., 2020. "Modelling multilevel data under complex sampling designs: An empirical likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:csdana:v:145:y:2020:i:c:s0167947319302610
    DOI: 10.1016/j.csda.2019.106906
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947319302610
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2019.106906?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    2. Skinner, Chris J. & de Toledo Vieira, Marcel, 2007. "Variance estimation in the analysis of clustered longitudinal survey data," LSE Research Online Documents on Economics 39106, London School of Economics and Political Science, LSE Library.
    3. Sanjay Chaudhuri & Mark S. Handcock & Michael S. Rendall, 2008. "Generalized linear models incorporating population level information: an empirical‐likelihood‐based approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 311-328, April.
    4. Ganzeboom, H.B.G. & de Graaf, P.M. & Treiman, D.J. & de Leeuw, J., 1992. "A standard international socio-economic index of occupational status," WORC Paper 92.01.001/1, Tilburg University, Work and Organization Research Centre.
    5. 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.
    6. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    7. J. Chen, 2002. "Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys," Biometrika, Biometrika Trust, vol. 89(1), pages 230-237, March.
    8. Danny Pfeffermann & Fernando Antonio Da Silva Moura & Pedro Luis Do Nascimento Silva, 2006. "Multi-level modelling under informative sampling," Biometrika, Biometrika Trust, vol. 93(4), pages 943-959, December.
    9. Wayne A. Fuller, 2009. "Some design properties of a rejective sampling procedure," Biometrika, Biometrika Trust, vol. 96(4), pages 933-944.
    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. 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.

    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. 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.
    2. Y. G. Berger & O. De La Riva Torres, 2016. "Empirical likelihood confidence intervals for complex sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 319-341, March.
    3. Denis Devaud & Yves Tillé, 2019. "Deville and Särndal’s calibration: revisiting a 25-years-old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1033-1065, December.
    4. Patricia Dörr & Jan Pablo Burgard, 2019. "Data-driven transformations and survey-weighting for linear mixed models," Research Papers in Economics 2019-16, University of Trier, Department of Economics.
    5. Joseph L Dieleman & Tara Templin, 2014. "Random-Effects, Fixed-Effects and the within-between Specification for Clustered Data in Observational Health Studies: A Simulation Study," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-17, October.
    6. Laura M. Stapleton & Yoonjeong Kang, 2018. "Design Effects of Multilevel Estimates From National Probability Samples," Sociological Methods & Research, , vol. 47(3), pages 430-457, August.
    7. Woojin Chung & Roeul Kim, 2020. "Which Occupation is Highly Associated with Cognitive Impairment? A Gender-Specific Longitudinal Study of Paid and Unpaid Occupations in South Korea," IJERPH, MDPI, vol. 17(21), pages 1-17, October.
    8. Nora Würz & Timo Schmid & Nikos Tzavidis, 2022. "Estimating regional income indicators under transformations and access to limited population auxiliary information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1679-1706, October.
    9. Yves G. Berger, 2016. "Empirical Likelihood Inference for the Rao-Hartley-Cochran Sampling Design," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 721-735, September.
    10. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    11. 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.
    12. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    13. 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.
    14. Glen McGee & Jonathan Schildcrout & Sharon‐Lise Normand & Sebastien Haneuse, 2020. "Outcome‐dependent sampling in cluster‐correlated data settings with application to hospital profiling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 379-402, January.
    15. Alinne Veiga & Peter W. F. Smith & James J. Brown, 2014. "The use of sample weights in multivariate multilevel models with an application to income data collected by using a rotating panel survey," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 65-84, January.
    16. Carla Cristina Rosa de Almeida & João Policarpo Rodrigues Lima & Maria Fernanda Freire Gatto, 2020. "Expenditure on cultural events: preferences or opportunities? An analysis of Brazilian consumer data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(3), pages 451-480, September.
    17. Joelle Abramowitz & Brett O'Hara & Darcy Steeg Morris, 2017. "Risking Life and Limb: Estimating a Measure of Medical Care Economic Risk and Considering its Implications," Health Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 469-485, April.
    18. Yves G. Berger, 2020. "An empirical likelihood approach under cluster sampling with missing observations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 91-121, February.
    19. Jan Pablo Burgard & Patricia Dörr, 2018. "Survey-weighted Generalized Linear Mixed Models," Research Papers in Economics 2018-01, University of Trier, Department of Economics.
    20. 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.

    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:eee:csdana:v:145:y:2020:i:c:s0167947319302610. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.