The Rank Transformation Test for Balanced Incomplete Block Designs.

Abstract

In Balanced Incomplete Block Designs the presence of outliers in the data not only indicate non-normality, so that classical methods of analysis on the data are inappropriate, but also diminish the power of classical methods of analysis that assume normality of the data. One alternative is to use Durbin's nonparametric test. This research examined a rank transformation approach where all the data are ranked from smallest to largest, over all treatments and blocks. Then the usual F-test is computed on the ranks. This rank transformation approach is asymptotically distribution-free, and is very powerful in cases where outliers are present as compared with both the parametric F-test and Durbin's nonparametric test. In the case of normality of the data it has only a slight loss of power. Thus it is a good alternative procedure to consider when analyzing data from a Balanced Incomplete Block Design.

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Document Details

Document Type
Technical Report
Publication Date
May 24, 1997
Accession Number
ADA328382

Entities

People

  • W. J. Conover

Organizations

  • Texas Tech University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Asymptotic Normality
  • Computational Science
  • Computer Simulations
  • Computing-Related Activities
  • Data Science
  • Data Sets
  • Experimental Design
  • Information Operations
  • Information Science
  • Interdisciplinary Science
  • Knowledge Management
  • Mathematical Analysis
  • Military Research
  • Normality
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.