How to Analyze Likert Scale Data? The most popular technique for sizing replies in survey investigations is the use of Likert scales.
The Likert scale is used in survey questions that ask you to rate your level of agreement, from strongly agree to strongly disagree.
The worksheet contains data for two groups using a five-point Likert scale.
How to Analyze Likert Scale Data
| Group1 | Group2 |
| 1 | 3 |
| 2 | 2 |
| 4 | 4 |
| 5 | 3 |
Although there is much discussion on how to interpret these data, Likert data appear to be excellent for survey items.
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The general question is whether to evaluate Likert data using a parametric or nonparametric test.
The majority of people have more experience with parametric testing. Unfortunately, Likert data have a narrow range, are ordinal, and discrete.
The majority of parametric tests’ assumptions are broken by these characteristics. The following are some of the key points raised in the discussion of utilizing various tests using Likert data:
Parametric analyses presume continuous data with a normal distribution. Although parametric tests can still be used with nonnormal data if the sample size is large enough(>30). A parametric test is a 2-sample t-test.
Nonparametric tests are reliable for ordinal data because they do not rely on the assumption of a normal distribution.
Nonparametric tests, however, worry some since they are less likely to find an impact that really occurs. A nonparametric test is one such as the Mann-Whitney test.
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How should data from a Likert scale be analyzed?
To evaluate the Mann-Whitney test and the 2-sample t-test to analyze data from a five-point Likert scale for two groups.
Let’s determine which of these statistical tests is more appropriate to employ!
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The results show that both types of analysis (Mann-Whitney test and the 2-sample t-test) yield type I error rates that are almost identical to the goal value after examining all pairs of distributions.
The simulation analysis demonstrates that there is just a slight variation in the statistical power between these two tests.
There was no basis for concerns that the Mann-Whitney test would be underpowered. The likelihood that the two tests will pick up on an actual difference between populations is typically equal.
Which of the two statistical analyses you apply to examine your Likert data doesn’t usually matter.
Both the 2-sample t-test and Mann-Whitney test have approximately equal type I error rates and power when evaluating five-point Likert data for two groups.
These outcomes hold true for groups of 10, 30, 100, and 200 people.
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