For researchers, correlation analysis is a familiar tool used to measure the strength and direction of the relationship between two variables. However, correlation analysis on non-parametric variables measured using the Likert scale has fundamental differences compared to correlation analysis on parametric variables.

This is due to the characteristics of the Likert scale, which is generally used to measure individual perceptions, attitudes, or subjective responses. In this article, Kanda Data will discuss how to conduct correlation analysis on non-parametric variables using the Likert scale.

**Understanding Data Measurement Scales in Statistics**

It is essential to understand the measurement scales of data in statistics, as this will influence the selection of the appropriate statistical tests. Data measurement scales can be divided into four categories: nominal scale, ordinal scale, interval scale, and ratio scale.

Each scale has different characteristics, which affect the type of statistical analysis that can be performed. In general, the nominal scale is used to categorize data without any particular order, while the ordinal scale categorizes data with a clear order.

The interval scale is a parametric measurement scale used for data that has equal intervals between values. In addition to intervals, the ratio scale also has an absolute zero point.

**Likert Scale Variables are Ordinal Scale**

The Likert scale, often used in surveys to measure levels of agreement or frequency, is an example of an ordinal scale. In this scale, respondents are asked to choose from a series of options that reflect their level of agreement or disagreement with a statement.

Although there is an order in the choices, such as from “strongly agree” to “strongly disagree,” the differences between the choices do not have a fixed numerical value. This makes Likert scale values classified as ordinal scale data. Therefore, the appropriate statistical analysis for Likert scale data must consider the characteristics of variables measured using the ordinal scale.

**Spearman’s Rank Correlation Test is the Right Choice**

For variables measured using the Likert scale, the appropriate correlation test is Spearman’s Rank Correlation. This test is designed to measure the strength and direction of the relationship between two ordinal-scale variables.

Unlike Pearson’s correlation, which assumes normal distribution and a linear relationship between variables, Spearman’s Rank Correlation does not make these assumptions. Spearman’s Rank Correlation converts the original values into ranks, then calculates the correlation based on these ranks.

This makes Spearman’s Rank Correlation more suitable for data that does not meet parametric assumptions, such as data generated from the Likert scale. Additionally, Spearman’s Rank Correlation test can also be used for parametric variables that are not normally distributed.

**Case Study Example: Correlation Between Employee Motivation and Performance**

For example, suppose we want to test whether there is a correlation between employee motivation and employee performance in a company. Both variables are measured using the Likert scale, where employee motivation is assessed through several questions evaluating motivation levels from “very unmotivated” to “very motivated,” and employee performance is measured from “very low” to “very high.”

Given that both variables are ordinal, the Spearman’s Rank Correlation test will be used to measure the relationship between motivation and performance. The results of this test can provide information on how strong the relationship is between employee motivation and their performance, which can be used as a basis for managerial decision-making.

**Conclusion**

In correlation analysis, it is important to choose the method that fits the type of data used. For non-parametric variables measured using the Likert scale, Spearman’s Rank Correlation test is the right choice.

By understanding the characteristics of the Likert scale as an ordinal scale and applying the appropriate test, correlation analysis can be conducted more accurately. This is the article that Kanda Data can provide at this time. Hopefully, it is useful for all of you. Stay tuned for the latest articles from Kanda Data next week.