Comparison of rank transformation test statistics with its nonparametric counterpart using reallife data
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Over the years, non-parametric test statistics have been the only solution to solve data
that do not follow a normal distribution. However, giving statistical interpretation used to
be a great challenge to some researchers. Hence, to overcome these hurdles, another
test statistics was proposed called Rank transformation test statistics so as to close the
gap between parametric and non-parametric test statistics. The purpose of this study is
to compare the conclusion statement of Rank transformation test statistics with its
equivalent non parametric test statistics in both one and two samples problems using
real-life data. In this study, (2018/2019) Post Unified Tertiary Matriculation Examinations
(UTME) results of prospective students of Ladoke Akintola University of Technology
(LAUTECH) Ogbomoso across all faculties of the institution were used for the analysis.
The data were subjected to nonparametric test statistics which include; Asymptotic
Wilcoxon sign test and Wilcoxon sum Rank (both Asymptotic and Distribution) using
Statistical Packages for Social Sciences (SPSS). In the same vein, R-statistical
programming codes were written for Rank Transformation test statistics. Their P-values
were extracted and compared with each other with respect to the pre-selected alpha
level (α) = 0.05. Results in both cases revealed that there is a significant difference in
the median of the scores across all faculties since their type I error rate are less than
the preselected alpha level 0.05. Therefore, Rank transformation test statistics is
recommended as alternative test statistics to non-parametric test in both one sample
and two-sample problems. © 2020 by authors, all rights reserved.
Keywords
BF Psychology