Pearson’s Correlation Coefficient: A Comprehensive Overview

Pearson’s correlation coefficient is a statistical measure that evaluates the strength and direction of the relationship between two continuous variables. It is considered the most effective method for assessing associations due to its reliance on covariance. This coefficient not only reveals the magnitude of the correlation but also its direction.

Key Assumptions:

Independence: Each case should be independent of others.

Linearity: There must be a linear relationship between the variables, which can be verified through a scatterplot. If the plot forms a straight line, the criterion is met.

Homoscedasticity: The scatterplot of residuals should approximate a rectangular shape.


Range: The coefficient’s value ranges from +1 (perfect positive correlation) to -1 (perfect negative correlation), with 0 indicating no correlation.

Unit Independence: The coefficient is unaffected by the units of measurement, ensuring comparability across different scales.

Symmetry: The correlation between two variables remains consistent, regardless of the variable order (X with Y or Y with X).

Degrees of Correlation:

Perfect: Values near ±1 indicate a perfect correlation, where one variable’s increase (or decrease) is mirrored by the other.

High Degree: Values between ±0.50 and ±1 suggest a strong correlation.

Moderate Degree: Values between ±0.30 and ±0.49 indicate a moderate correlation.

Low Degree: Values below +0.29 are considered a weak correlation.

No Correlation: A value of zero implies no relationship.

Further Reading:

Conduct and Interpret a Bivariate (Pearson) Correlation

Correlation Analysis (Pearson, Kendall, Spearman)

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