What are the Common Measures of Association in Statistics?
05/03/2024 | by Patrick Fischer, M.Sc., Founder & Data Scientist: FDS
Introduction
Measures of association play a central role in statistical analysis to quantify the relationship between two or more variables. There are various measures of association used depending on the type of data and the relationships between the variables. This article provides an overview of the common measures of association in statistics.
Common Measures of Association
- Pearson's Correlation Coefficient (r): Measures the linear relationship between two continuous variables.
- Spearman's Rank Correlation Coefficient (ρ): Assesses the monotonic relationship between two variables and is suitable for ordinal data.
- Kendall's Tau: Similar to Spearman's ρ, evaluates the rank correlation between two variables.
- Chi-Square Test for Independence (χ²): Measures the relationship between two categorical variables.
- Point-Biserial Correlation: Assesses the relationship between one continuous and one dichotomous variable.
- Phi Coefficient (φ): A measure of association between two dichotomous variables.
- Cramér's V: An extension of the chi-square test to measure the strength of association between two categorical variables.
Conclusion
In statistics, there are various measures of association that can be selected depending on the type of data and the nature of the relationship between variables. Understanding these measures and their applications is crucial for correct and meaningful data analysis and interpretation.