We always keep reiterating to students that statistics is omnipresent, as long as it is in the domain of data. There are statistical analysis methods to analyze any kind of data. We can analyze images, text, and even none quantifiable things such as your ratings for a movie in IMDB. You know about statistical tools such as t-test and ANOVA and know that there are some cases where they are not applicable. An example is when you are using ordinal data. What are the best statistical tests in such a situation?
Non parametric test like the ANOVA makes an assumption about the distribution of the data. In the ANOVA test, the data has to be normally distributed and if it’s not; you have the option of transforming the data into a normal distribution or using another test. Even so, there are those kinds of data that can’t be normalized. In such a case, you should use a non-parametric test.
When can I use non-parametric tests
There are several instances where you can use these tests, they include:-
1. When you are using ordinal data. Parametric tests are suited to conducting experiments on quantitative datasets. Ordinal data require a non-parametric test. Ordinal data are the numerical values used to express qualitative data.
2. Sample size. In some cases, the sample size might be too small. For such types of data, it may be appropriate to use non-parametric tests even though the parametric analysis may be considered necessary. The reason is that non-parametric tests give more accurate results when the datasets are small, but their inaccuracies increase with an increase in data.
3. Non-compliance with the distribution assumptions.
As we had previously mentioned, they are suited for datasets that cannot meet the normality assumptions. To know if the data is normally distributed, we can use visualization methods such as histogram or quantity plots. For accurate results, statistical tests such as Shapiro-Wilk can be used.
Types of non-parametric tests.
Kruskal-Wallis test is a non-parametric test, an equivalent of the one-way ANOVA parametric test. It’s used to test if the samples from the same distribution and compares two or more samples.
Mann-Whitney U test
Mann-Whitney U test is a non-parametric test used to compare the differences in two samples groups
Spearman rank correlation
Surprisingly, the Spearman rank correlation is a non-parametric test. It’s used to measure the correlation between tri variables. What makes it ideal for this case is that it can be used for the example where the data does not follow a normal distribution and for ordinal data.
It’s a test which is a non-parametric equivalent of the one-way anova repeated measures. It tests for the difference between ordinal data groups.
Advantages of non-parametric tests
They are suitable for small sample sizes.
They do not have lots of assumptions. therefore their analysis becomes simpler
They can be used for all data types.
They are appropriate when statistical assumptions have been violated.
Non-parametric analysis using SPSS.
What makes SPSS the most preferred statistical software is its ease of use. Everything is straightforward with the software. With a little guideline, you could be doing some of the most complicated statistical analysis. Further, the environment is user friendly. You do not need a lot of explanation to know what to do. It’s also capable of doing any mathematical task and is freely available, making it easy to access. Documentation related to software is available all over the net.
For the non-parametric test in SPSS, go to analyze and then select non-parametric test. There are different types of tests that you can apply from the options that appear.
Non-parametric analysis Assignment help.
Now that you know the different types of non-parametric tests, you can go ahead with your analysis. In case you find any challenges, contact us even if it is just a question that you need to seek clarification. In addition to assignment help, our online non-parametric analysis assignment helpers will offer you instant support.
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