Pearson’s correlation coefficient is a statistical measure that not only evaluates the strength but also direction of the relationship between two continuous variables. Researchers consider it 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.
Independence: Each case, therefore, should be independent of others.
Linearity: There must be a linear relationship between the variables, which, in addition, can be verified through a scatterplot. When a straight line forms in the plot, we will meet the criterion.
Homoscedasticity: The scatterplot of residuals, consequently, should approximate a rectangular shape.
Characteristics:
Range: The coefficient’s value ranges from +1 (perfect positive correlation) to -1 (perfect negative correlation), with 0, on the other hand, indicating no correlation.
Unit Independence: The coefficient ensures comparability across different scales because it is not affected by the units of measurement.
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, meaning that an increase (or decrease) in one variable corresponds directly to an increase (or decrease) in 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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