Using real life data to validate the winsorized modified Alexander-Govern test

To evaluate the efficiency and reliability of the Alexander-Govern (AG) test and the Winsorized Modified One Step M-estimator in the Alexander-Govern (AGWMOM) test, using real life data. Methods: Test of homogeneity of variance was done from real life data, comprising of young, middle and old groups...

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Bibliographic Details
Main Authors: Ochuko, Tobi Kingsley, Abdullah, Suhaida, Zain, Zakiyah, Syed Yahaya, Sharipah Soaad
Format: Article
Language:English
Published: Scientific & Academic Publishing. All Rights Reserved 2016
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Online Access:https://repo.uum.edu.my/id/eprint/18469/1/IJSA%206%202%202016%2045-52.pdf
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Summary:To evaluate the efficiency and reliability of the Alexander-Govern (AG) test and the Winsorized Modified One Step M-estimator in the Alexander-Govern (AGWMOM) test, using real life data. Methods: Test of homogeneity of variance was done from real life data, comprising of young, middle and old groups, using the Levene’s test to see if the three groups are different from each other or not as the reaction time changes.Descriptive statistics, Test of normality and Test Statistic were performed for the three independent groups, to evaluate the reliability and efficiency of the tests. Results: The p-value from the test of homogeneity of the variance is greater than 0.05, i.e 0.174 > 0.05 and it shows that we accept HO and conclude that there is no difference between the groups as the reaction time changes.The descriptive statistics show that the AGWMOM test has a smaller standard error compared to the AG test.The result of the test statistic reveals that the AGWMOM test produced a p-value of 0.0000002869 that is considered to be significant compared to the AG test that produced a p-value of 0.0698 that is regarded as not significant, since its p-value is > 0.05. Conclusions: The AGWMOM test is more efficient and reliable in minimizing error as much as possible from the real life data, because the test produced a smaller standard error from the real life data in comparison to the AG test and is regarded as significant.