What is the Difference Between Association and Correlation?
🆚 Go to Comparative Table 🆚The terms association and correlation are often used to describe the relationship between two variables. However, they have slightly different meanings:
- Association: This refers to the presence of a relationship between two variables, meaning that certain values of one variable tend to co-occur with certain values of the other variable. Association can be used to describe any relationship, whether it's linear or non-linear.
- Correlation: This is a more specific term that quantifies the relationship between two random variables using a number between -1 and 1, typically referring to the Pearson Correlation Coefficient. Correlation is used to describe linear relationships between variables.
Some key differences between association and correlation include:
- Association is a general term that can be used to describe any relationship between variables, while correlation specifically refers to linear relationships.
- Association does not use a specific numerical scale to quantify the relationship between variables, while correlation coefficients do.
- Association does not imply causality, while correlation does not imply causality either.
In summary, association refers to any relationship between two variables, while correlation specifically refers to linear relationships between variables and quantifies the strength of the association with numerical values.
Comparative Table: Association vs Correlation
The difference between association and correlation can be summarized in the following table:
| Feature | Association | Correlation |
|---|---|---|
| Definition | Association refers to the relationship between two variables, often represented in a contingency table or a two-way relative frequency table. | Correlation is a statistical measure that indicates how strongly two variables are related, typically expressed as a correlation coefficient. |
| Purpose | Association helps to identify relationships between categorical variables, often used for two-way relative frequency tables. | Correlation helps to quantify the strength and direction of the relationship between two continuous or discrete variables. |
| Structure | Association tables (or contingency tables) are used to display the relationship between two variables, often showing frequencies or percentages of each category. | Correlation coefficients are used to quantify the strength and direction of the relationship between two variables, often ranging from -1 to 1 (-1 indicating a strong negative correlation, 1 indicating a strong positive correlation, and 0 indicating no correlation). |
| Examples | A university survey collects data on students' opinions of campus housing and students' gender. The resulting contingency table shows the association between gender and opinion. | After measuring the height and weight of a group of people, calculating the correlation coefficient between height and weight quantifies the strength and direction of their relationship. |
In summary, association is a qualitative measure used to describe the relationship between two categorical variables, often represented in a contingency table or a two-way relative frequency table. On the other hand, correlation is a quantitative measure used to describe the strength and direction of the relationship between two continuous or discrete variables, often expressed as a correlation coefficient.
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- Descriptive vs Correlational Research
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- Classification vs Regression
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