<![CDATA[Correlation is one of the statistical methods used to measure the relationship between two variables. Two commonly used types of correlation are Spearman Correlation and Pearson Correlation. Spearman Correlation is used for ordinal data or data that do not meet the assumption of normality, while Pearson Correlation is applied to interval or ratio data that follow a normal distribution. This study aims to compare the results of analyses using these two correlation methods through the SPSS software. By analyzing both simulated and real-world data, tests were conducted on various data scenarios, including variations in data types, distribution patterns, and levels of variable relationships. The results indicate that Pearson Correlation is more sensitive to normally distributed data, whereas Spearman Correlation provides more stable results for non-normally distributed data or data containing outliers. This article also presents a step-by-step guide for using SPSS to perform analyses with both correlation methods, making it easier for readers, especially students and researchers, to apply the appropriate method based on their data characteristics. By understanding the differences and advantages of each method, users are expected to choose the right approach for correlation analysis. ]]>
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