IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v20y2019i1p153-169n6.html
   My bibliography  Save this article

An Alternative Matrix Transformation To The F Test Statistic For Clustered Data

Author

Listed:
  • Intarapak Sukanya

    (Department of Mathematics, Faculty of Science, Srinakharinwirot University, Bangkok, Thailand .)

  • Supapakorn Thidaporn

    (Department of Statistics, Faculty of Science, Kasetsart University, Bangkok, Thailand .)

Abstract

For the regression analysis of clustered data, the error of cluster data violates the independence assumption. Consequently, the test statistic based on the ordinary least square method leads to incorrect inferences. To overcome this issue, the transformation is required to apply to the observations. In this paper we propose an alternative matrix transformation that adjusts the intra-cluster correlation with Householder matrix and apply it to the F test statistic based on generalized least squares procedures for the regression coefficients hypothesis. By Monte Carlo simulations of the balanced and unbalanced data, it is found that the F test statistic based on generalized least squares procedures with Adjusted Householder transformation performs well in terms of the type I error rate and power of the test.

Suggested Citation

  • Intarapak Sukanya & Supapakorn Thidaporn, 2019. "An Alternative Matrix Transformation To The F Test Statistic For Clustered Data," Statistics in Transition New Series, Statistics Poland, vol. 20(1), pages 153-169, March.
    • Handle: RePEc:vrs:stintr:v:20:y:2019:i:1:p:153-169:n:6
    DOI: 10.21307/stattrans-2019-009
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2019-009
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2019-009?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. Rao, J. N. K. & Wang, S. G., 1995. "The Power of F Tests Under Regression Models with Nested Error Structure," Journal of Multivariate Analysis, Elsevier, vol. 53(2), pages 237-246, May.
    2. Sandra M. Eldridge & Obioha C. Ukoumunne & John B. Carlin, 2009. "The Intra‐Cluster Correlation Coefficient in Cluster Randomized Trials: A Review of Definitions," International Statistical Review, International Statistical Institute, vol. 77(3), pages 378-394, December.
    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. Sukanya Intarapak & Thidaporn Supapakorn, 2019. "An Alternative Matrix Transformation To The F Test Statistic For Clustered Data," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 153-174, March.
    2. Michael P. Leung, 2023. "Cluster-Randomized Trials with Cross-Cluster Interference," Papers 2310.18836, arXiv.org, revised Nov 2024.
    3. Masahiro Kojima & Tatsuya Kubokawa, 2013. "Bartlett Adjustments for Hypothesis Testing in Linear Models with General Error Covariance Matrices," CIRJE F-Series CIRJE-F-884, CIRJE, Faculty of Economics, University of Tokyo.
    4. Anneke Vang Hjort & Mirte A. G. Kuipers & Maria Stage & Charlotta Pisinger & Charlotte Demant Klinker, 2022. "Intervention Activities Associated with the Implementation of a Comprehensive School Tobacco Policy at Danish Vocational Schools: A Repeated Cross-Sectional Study," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    5. Sheng Wu & Weng Kee Wong & Catherine M. Crespi, 2017. "Maximin optimal designs for cluster randomized trials," Biometrics, The International Biometric Society, vol. 73(3), pages 916-926, September.
    6. Simon D French & Joanne E McKenzie & Denise A O'Connor & Jeremy M Grimshaw & Duncan Mortimer & Jill J Francis & Susan Michie & Neil Spike & Peter Schattner & Peter Kent & Rachelle Buchbinder & Matthew, 2013. "Evaluation of a Theory-Informed Implementation Intervention for the Management of Acute Low Back Pain in General Medical Practice: The IMPLEMENT Cluster Randomised Trial," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-15, June.

    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:vrs:stintr:v:20:y:2019:i:1:p:153-169:n:6. 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: Peter Golla (email available below). General contact details of provider: https://stat.gov.pl/en/ .

    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.