Likert scale
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For Likert scale, what are the suitable analysis including significant tests?


Likert is a bipolar scale which can contain for both positive and negative response of a statement in SPSS research. Responses of this scale usually considered as ordinal data. In this scale, the questions are suitable to measure mean that can give you a sense in which direction most of the people’s opinion took place. Furthermore, the standard deviation can gives an important piece of information about the distance, means average distance from the mean. A lowlevel standard deviation will tell you the most observations crowd together around the mean. Similarly, high standard deviation would express the heterogeneity of responses.
In SPSS research, for comparing means between groups T test can be use. This scale comprise question with only 3/5/7 possible answers. However, this cannot possess a distribution which is under normal probability. The range of answers is not continuous, it is discrete. So the responses can’t distributed normally. Therefore, researcher should check the frequency and have to sure that distribution is knoll shaped. You shouldn’t run this test if the distribution is not mound shaped. The T test does not give you hard scientific proof, but it gives you the indication of the trend of the data. Moreover, if you have a set of pair response then you can also use paired sample T test. Lastly, for comparing mean between several groups you can use one way ANOVA.
For non parametric test you can use the Mann–Whitney test, Wilcoxon signedrank test, or Kruskal–Wallis test for comparing within groups in your SPSS research. Responses of Likert scale can be summed and after that you can divide this summed score into quartile or percentile portions. Furthermore, data can be transfer to the nominal scale like ‘accept’ or ‘reject’ options. Chisquare or McNemar test can be applied after having this transformation. Research can do more analysis, but he has to be aware that he should answer his research questions meaningfully.