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Author
Oladugba Abimibola Victoria (University of Nigeria, Nsukka, Nigeria), Obasi Ajali John (University of Nigeria, Nigeria), Asogwa Oluchukwu Chukwuemeka (Alex Ekwueme Federal University Ndufu Alike Ikwo, Nigeria)
Title
Robustness of Randomisation Tests as Alternative Analysis Methods for Repeated Measures Design
Source
Statistics in Transition, 2022, vol. 23, nr 4, s. 77-90, tab., bibliogr. 24 poz.
Keyword
Symulacja Monte Carlo, Metody statystyczne, Testy
Monte Carlo simulation, Statistical methods, Tests
Note
summ.
Abstract
Randomisation tests (R-tests) are regularly proposed as an alternative method of hypothesis testing when assumptions of classical statistical methods are violated in data analysis. In this paper, the robustness in terms of the type-I-error and the power of the R-test were evaluated and compared with that of the F-test in the analysis of a single factor repeated measures design. The study took into account normal and non-normal data (skewed: exponential, lognormal, Chi-squared, and Weibull distributions), the presence and lack of outliers, and a situation in which the sphericity assumption was met or not under varied sample sizes and number of treatments. The Monte Carlo approach was used in the simulation study. The results showed that when the data were normal, the R-test was approximately as sensitive and robust as the F-test, while being more sensitive than the F-test when data had skewed distributions. The R-test was more sensitive and robust than the F-test in the presence of an outlier. When the sphericity assumption was met, both the R-test and the F-test were approximately equally sensitive, whereas the R-test was more sensitive and robust than the F-test when the sphericity assumption was not met.(original abstract)
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The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
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Bibliography
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ISSN
1234-7655
Language
eng
URI / DOI
http://dx.doi.org/10.2478/stattrans-2022-0043
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