The article focuses on discussing the ways of conducting the Kruskal-Wallis Test to progress in the research through in-depth data analysis and critical programme evaluation. The Kruskal–Wallis test by ranks, Kruskal–Wallis H test, or one-way ANOVA on ranks is a non-parametric method where the researchers can test whether the samples originate from the same distribution or not. It is mainly used for comparing two or more independent samples of equal or different sample sizes. There are advantages of using the Kruskal-Wallis Test as well as ways to apply this test in the research or thesis is which will be discussed further.
The Kruskal-Wallis Test can be utilised to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable. The significance level of the impendent variables can be analysed through this test, and the researchers try to identify the impacts of independent variables on their dependent one in the data set to test the hypothesis. The Kruskal-Wallis (and Mann-Whitney) can be seen technically as a comparison of the mean ranks, where ANOVA is different as ANOVA is explicitly a test of equality of means of values. For example, the researchers can use a Kruskal-Wallis H test to evaluate whether exam performance, measured on a continuous scale from 0-100, differed based on the test anxiety levels and in this case, the exam performance is considered to be a dependent variable and the independent variable is test anxiety level. There are three independent groups, which are low, medium and high to test the anxiety level of the students during the exam. The dependent variable in the data set is measured at the ordinal or continuous level, for example, the Likert scale is utilised for ordinal variables and revision time is considered for continuous variables. The Independent variable should consist of two or more categorical, independent groups and this test is useful to analyse the significance value of each independent variable group in the data set.
In SPSS, it is also possible to test the Kruskal-Wallis H by legacy dialogue and K impendent samples. Clicking analysis, a nonparametric test, legacy dialogues and K independent samples, which display a dialogue box, test for several independent samples. There is a test variable list as well as grouping variables, where the categories of independent variables must be included step by step. There would be defining the range, with maximum and minimum values as per the range of codes that the researchers give to the independent variables. After clicking on the decretive test, there will be median and quartiles in the result. This is hereby beneficial for the researchers to compare the samples, whether they are originated from the same distribution or other. The mean rank column represents the relationship between the dependent and the independent variable through which the researchers can assess the effects of each independent group on the dependent variable. Hence, the researchers can test the hypothesis through Kruskal-Wallis H, where the mean and quartiles are measured by considering the data sets.
It can be stated that the Kruskal-Wallis H test is effective for the researchers to analyse the influence of the independent variables on the decedent variable in the data set and it also provides a scope to consider all the groups and categories of the independent variables to explore the effects of each group in the data set that influence the dependent variable. Hereby, in statistical analysis, Kruskal-Wallis H is beneficial to perform descriptive analysis as well as test the hypothesis for making decisions. The researchers can implement the Kruskal-Wallis H by utilising SPSS software to sort the data and input all the independent variables with different groups for critical analysis. At SPSS tutor, in our enhanced Kruskal-Wallis H test guide we show you how to run a Kruskal Wallis Nonparametric Tests > K Independent Samples procedure in SPSS data analysis, which includes a post hoc test so that you can determine the differences lie between your groups. We help you in writing your results so that in future if you need to report these in a dissertation & thesis writing or research report.
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