Spearman’s Rank Correlation Coefficient is a non-parametric statistical method used to measure the degree and direction of association between two variables based on their ranks rather than their actual numerical values. It is particularly useful in quantitative analysis when the data do not satisfy the assumptions required for Pearson’s correlation coefficient, such as normal distribution or linear relationships. In managerial applications, Spearman’s Rank Correlation Coefficient helps in analyzing relationships between qualitative or ordinal variables and supports decision-making processes. Spearman’s Rank Correlation Coefficient is represented by the symbol “ρ” (rho) and is calculated by ranking the observations of two variables separately and then comparing the differences between their ranks. The coefficient indicates whether the relationship between the two variables is positive, negative, or absent. The value of Spearman’s coefficient ranges from +1 to -1. A value of +1 represents a perfect positive correlation, meaning that both variables increase or decrease together in the same order. A value of -1 represents a perfect negative correlation, indicating that as one variable increases, the other decreases. A value close to zero indicates that there is little or no relationship between the variables. The formula for calculating Spearman’s Rank Correlation Coefficient is: ρ = 1 – (6Σd² / n(n² – 1)) where ρ represents Spearman’s rank correlation coefficient, d represents the difference between the ranks of corresponding observations, and n represents the total number of observations.