Spearman's rank correlation is a non-parametric measure of the strength and direction of the association between two ranked variables. Unlike
Pearson's correlation coefficient, it does not assume a linear relationship or normally distributed data. Instead, it assesses how well the relationship between two variables can be described using a monotonic function.
In
epidemiological studies, data often do not meet the assumptions required for parametric tests. For example, the relationship between two variables may not be linear, or the data may contain
outliers and skewed distributions. Spearman's rank correlation is useful in these cases because it ranks data and thereby reduces the impact of outliers and does not require the data to be normally distributed.
Spearman's rank correlation coefficient (ρ or rs) is calculated using the following steps:
Rank the data points for each variable.
Calculate the difference (d) between the ranks of each pair of observations.
Square these differences (d²).
Use the formula:
Spearman's rank correlation can be used in various epidemiological applications, including:
Risk factor analysis: Assess the relationship between potential risk factors and health outcomes.
Ecological studies: Explore associations between environmental exposures and disease prevalence across different regions.
Genetic epidemiology: Investigate the association between genetic markers and diseases.
Behavioral studies: Examine the correlation between health behaviors and disease incidence.
Does not require data to be normally distributed.
Less affected by outliers.
Can handle ordinal data.
Disadvantages
Less powerful than parametric tests like Pearson's correlation when data meet parametric assumptions.
Only assesses monotonic relationships, not necessarily linear ones.
The value of Spearman's rank correlation coefficient ranges from -1 to +1. A coefficient of +1 indicates a perfect positive association between the ranks, -1 indicates a perfect negative association, and 0 indicates no association. In epidemiological research, the strength of the association can be interpreted as follows:
0 to 0.19: Very weak
0.20 to 0.39: Weak
0.40 to 0.59: Moderate
0.60 to 0.79: Strong
0.80 to 1.0: Very strong
Conclusion
Spearman's rank correlation is a valuable tool in epidemiology for assessing the strength and direction of associations between variables, especially when data do not meet the assumptions required for parametric tests. Its application ranges from
risk factor analysis to ecological and genetic studies, offering robust insights into the relationships between various health determinants and outcomes.
