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# How to Compute Spearman Rank Correlation Test

A correlation test is still often an option to solve problems in research. The correlation test includes the Pearson correlation test, Spearman rank correlation test, and chi-square test. Determining the type of correlation test to use depends on the measurement data scale. Well, on this occasion, I will discuss using the Spearman rank correlation test.

Before discussing it in more depth, so that you better understand how to test the Spearman rank correlation test, it would be better to discuss what the Spearman rank correlation test is. As previously stated, the correlation test is quite popularly used to solve association relationships between observed variables. So, you can say that this correlation test is intended to determine how the relationship between one variable is with another variable.

The difference with regression is that regression is conducted to determine the effect of one variable on other variables. Therefore, the correlation is intended to determine the relationship between variables. For example, if X is correlated with Y, then Y is correlated with X. This will be different from a regression where X affects Y, but not necessarily Y affects X.

That is the concept of the correlation test that needs to be well understood. The next question is, “when can we use the Spearman rank correlation test?” For those who have done research using this analysis, you must have understood the concept. We return to the data measurement scale of the variables we observe.

Spearman rank correlation test is used when the data scale of the variables to be tested has an ordinal scale. This type of measurement scale is used on non-parametric variables, such as education, attitudes, competence, behavior, and other similar variables. In these variables, the data is obtained indirectly in numerical (parametric) form, so that to be analyzed, we need to categorize. Ordinary scale data will differ from numerical measurement scales, such as interval and ratio scales. If you still don’t understand how to tell the difference, I’ve made an audiovisual; you can watch it below (video in Indonesian, please use English translation):

Have you watched the video? If you have watched, I hope you can understand the different types of data measurement scales, both nominal, ordinal, interval, and ratio. So, to process the non-parametric variables, we need to categorize them. The most familiar is to use the Likert scale. Each variable will be represented in several items on a Likert scale statement/question, and the results will be in the form of scores for each variable. Finally, the variables such as education, attitude, competence, and behavior can be obtained with quantitative figures so that data analysis can be carried out.

The scores of all statements on the Likert scale are added up for each observed variable. For example, the competency variable consists of 10 statements on a Likert scale of 1-5. If a respondent answers all the items on the Likert scale statement, a minimum score of 10 will be obtained and a maximum score of 50 for that variable. After the variable data is in scores, the Spearman rank correlation analysis can be computed.

Spearman rank correlation analysis can be easily determined independently using statistical software or manually calculated using a calculator or excel. How to analyze the Spearman rank correlation and how to interpret it can be seen in the following video (video in Indonesian, please use English translation):

Based on the video you have watched, you can see that in interpreting the Spearman rank correlation analysis results, three main things need to be elaborated: the statistical significance of the variable relationship, the direction of the relationship, and the closeness of the relationship. In determining statistical significance, we need to determine whether the statistical hypothesis is at 5%, 1%, or 10% alpha.

So Spearman rank correlation can be used to test the relationship between variables with ordinal data scale measurement. The interpretation of correlation analysis results includes the significance test, the direction of the relationship, and the closeness of the relationship.

The article on this occasion I suffice. Hopefully, it will provide added value for you wherever you are to enrich research analysis methods. If you need more audiovisuals related to statistics, econometrics, and data, you can visit the “Kanda Data” youtube channel. See you in the next article!

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