Zahayu Md Yusof

An application of importance satisfaction analysis (ISA) method in improving the internship programme for Malaysia Higher Learning Institution

Nowadays most of the university students are required to undergo internship as a requirement before graduation. Internship is very important for students to practice what they have learned in the classroom. During internship students are exposed to the actual situation of how to deal with customers and suppliers which can provide added value to the students. In the choice of company for internship, students also consider a number of things such as internship allowances, work environment, interesting work, stable work shift and other fridge benefits. Study on the importance and satisfaction of students is important to improve the internship program. Importance means how students feel important to the attributes and satisfaction means how students feel after undergoing internship. The aim of this study is to investigate the gaps between students’ important and satisfaction on the internship programme and to identify the internship experience factors that need to be improved. Gap analysis has been used to sh...

Modified H-statistic with adaptive Winsorized mean in two groups test

t-test is a commonly used test statistics when comparing two independent groups. The computation of this test is simple yet it is powerful under normal distribution and equal variance dataset. However, in real life data, sometimes it is hard to get dataset which has this package. The violation of assumptions (normality and equal variances) will give the devastating effect on the Type I error rate control to the t-test. On the same time, the statistical power also will be reduced. Therefore in this study, the adaptive Winsorised mean with hinge estimator in H-statistic (AWM-H) is proposed. The H-statistic is one of the robust statistics that able to handle the problem of nonnormality in comparing independent group. This procedure originally used Modified One-step M (MOM) estimator which employed trimming process. In the AWM-H procedure, the MOM estimator is replaced with the adaptive Winsorized mean (AWM) as the central tendency measure of the test. The Winsorization process is based on hinge estimator HQ ...

Testing the equality of students’ performance using Alexander-Govern test with adaptive trimmed means

Analyzing the equality of independent group has to be done with caution. The classical approaches such as ttest for two groups and analysis of variance (ANOVA) for more than two groups always are favorable selection by researchers. However, sometime these methods were abused by the presence of nonnormality or variance heterogeneity or both. It is known that ANOVA is restricted to the assumptions of normality and homogeneity of variance. In real life data, sometimes these requirements are hard to attain. The Alexander-Govern test with adaptive trimmed mean (AG_atm) is one approach that can be chosen as alternative to the classical tests when their assumptions are violated. In this paper, the performances of AG_atm were compared to the original AG test and ANOVA using simulated and real life data. The simulation study proved that the AG_atm performs better than the original AG test and the classical test. For real life data, student’s performance in decision analysis course, measured by final examination sc...

H-statistic with winsorized modified one-step M-estimator for two independent groups design

Two-sample independent t-test is a classical method which is widely used to test the equality of two groups. However, this test is easily affected by any deviation in normality, more obvious when heterogeneity of variances and group sizes exist. It is well known that the violation in the assumption of these tests will lead to inflation in Type I error rate and depression in statistical test power. In mitigating the problem, robust methods can be used as alternatives. One such method is H-statistic. When used with modified one-step M-estimator (MOM), this test statistic (MOM-H) produce good control of Type I error even under small sample size but inconsistent across certain conditions investigated. Furthermore, power of the test is low which might be due to the trimming process. In this study, MOM is winsorized (WMOM) to sustain the original sample size. The H-statistic with WMOM as the central tendency measures (denoted as WMOM-H) showed better control of Type I error as compared to MOM-H especially under...

Robust parametric bootstrap test with MOM estimator: An alternative to independent sample t-test

Normality and homogeneity are two major assumptions that need to be fulfilled when using independent sample t-test. However, not all data encompassed with these assumptions. Consequently, the result produced by independent sample t-test becomes invalid. Therefore, the alternative is to use robust statistical procedure in handling the problems of nonnormality and variances heterogeneity. This study proposed to use Parametric Bootstrap test with popular robust estimators, MADn and Tn which empirically determines the amount of trimming. The Type I error rates produced by each procedure were examined and compared with classical parametric test and nonparametric test namely independent sample t-test and Mann Whitney test, respectively. 5000 simulated data sets are used in this study in order to generate the Type I error for each procedure. The findings of this study indicate that the Parametric Bootstrap test with MADn and Tn produces the best Type I error control compared to the independent sample t-test and ...

P-method post hoc test for adaptive trimmed mean, HQ

Adaptive trimmed mean, HQ, which is one of the latest additions in robust estimators, had been proven to be good in controlling Type I error in omnibus test. However, post hoc (pairwise multiple comparison) procedure for HQ was yet to be developed then. Thus, we have taken the initiative to develop post hoc procedure for HQ. Percentile bootstrap method or P-Method was proposed as it was proven to be effective in controlling Type I error rate even when the sample size was small. This paper deliberates on the effectiveness of P-Method on HQ, denoted as P-HQ. The strength and weakness of the proposed method were put to test on various conditions created by manipulating several variables such as shape of distributions, number of groups, sample sizes, degree of variance heterogeneity and pairing of sample sizes and group variances. For such, a simulation study on 2000 datasets was conducted using SAS/IML Version 9.2. The performance of the method on various conditions was based on its ability in controlling Ty...