UNDERSTANDING
CORRELATIONAL DESIGNS
• Exploring relationships between variables is a fundamental aspect of statistical analysis in
psychology and many other disciplines
Pearson's r (Pearson Product-Moment Correlation Coefficient):
Pearson's r measures the linear relationship between two continuous, normally distributed
variables. It quantifies both the direction (positive or negative) and the strength of the linear
relationship on a scale from -1 to 1.
Interpretation:
1. A value of 1 indicates a perfect positive linear relationship.
2. A value of -1 indicates a perfect negative linear relationship.
3. A value of 0 indicates no linear relationship.
4. Values close to 1 or -1 indicate a strong relationship, while values close to 0 indicate a weak
relationship.
Assumptions: The main assumptions behind Pearson's r include linearity (the relationship forms a
straight line), homoscedasticity (the variability in one variable is consistent across all values of
the other variable), and normality (both variables are normally distributed).
Spearman's rho (Spearman's Rank-Order Correlation):
Spearman's rho is a non-parametric measure of rank correlation. It assesses how well the
relationship between two variables can be described using a monotonic function, without
making assumptions about the frequency distribution of the variables.