IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v85y2020i3d10.1007_s11336-020-09713-6.html
   My bibliography  Save this article

Traditional and Rank-Based Tests for Ordered Alternatives in a Cluster Correlated Model

Author

Listed:
  • Yuanyuan Shao

    (General Motors)

  • Joseph W. McKean

    (Western Michigan University)

  • Bradley E. Huitema

    (Western Michigan University)

Abstract

Methods for the analysis of one-factor randomized groups designs with ordered treatments are well established, but they do not apply in the case of more complex experiments. This article describes ordered treatment methods based on maximum-likelihood and robust estimation that apply to designs with clustered data, including those with a vector of covariates. The contrast coefficients proposed for the ordered treatment estimates yield higher power than those advocated by Abelson and Tukey; the proposed robust estimation method is shown (using theory and simulation) to yield both high power and robustness to outliers. Extensions for nonmonotonic alternatives are easily obtained.

Suggested Citation

  • Yuanyuan Shao & Joseph W. McKean & Bradley E. Huitema, 2020. "Traditional and Rank-Based Tests for Ordered Alternatives in a Cluster Correlated Model," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 531-554, September.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09713-6
    DOI: 10.1007/s11336-020-09713-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-020-09713-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-020-09713-6?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. Kloke, John D. & McKean, Joseph W. & Rashid, M. Mushfiqur, 2009. "Rank-Based Estimation and Associated Inferences for Linear Models With Cluster Correlated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 384-390.
    2. Thomas Hettmansperger & Joseph McKean, 1978. "Statistical inference based on ranks," Psychometrika, Springer;The Psychometric Society, vol. 43(1), pages 69-79, March.
    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. Yahia S El-Horbaty & Eman M Hanafy, 2018. "Some Estimation Methods and Their Assessment in Multilevel Models: A Review," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(3), pages 69-76, February.
    2. Jinguo Gao & Omer Ozturk, 2017. "Rank regression in order restricted randomised designs," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 231-257, April.
    3. Wallace, R. S. Olusegun & Choudhury, Mohammed S. I. & Adhikari, Ajay, 1999. "The Comprehensiveness of Cash Flow Reporting in the United Kingdom: Some Characteristics and Firm-specific Determinants," The International Journal of Accounting, Elsevier, vol. 34(3), pages 311-347, August.
    4. Roberto F. Manghi & Gilberto A. Paula & Francisco José A. Cysneiros, 2016. "On elliptical multilevel models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2150-2171, September.
    5. Riina Lemponen & Denis Larocque & Jaakko Nevalainen & Hannu Oja, 2012. "Weighted rank tests and Hodges-Lehmann estimates for the multivariate two-sample location problem with clustered data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 977-991, December.
    6. Liya Fu & You-Gan Wang, 2012. "Efficient Estimation for Rank-Based Regression with Clustered Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1074-1082, December.
    7. Melody Denhere & Huybrechts F. Bindele, 2016. "Rank estimation for the functional linear model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(10), pages 1928-1944, August.
    8. Donald R. Epley, 1997. "A Note on the Optimal Selection and Weighting of Comparable Properties," Journal of Real Estate Research, American Real Estate Society, vol. 14(2), pages 175-182.

    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:spr:psycho:v:85:y:2020:i:3:d:10.1007_s11336-020-09713-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.