Correlation is the measure of how strongly two variables are related to each other. It can be measured by the Pearson correlation coefficient (r), which ranges from -1 to 1. It is important to study correlation because it can help us understand the relationship between two or more variables, and it can also help us predict future outcomes. Let’s see different types of correlation.
Topics Discussed in This Article:
- Meaning of Correlation
- Types of Correlation
- Examples of Correlation
- Uses of Correlation
- Why is Correlation Analysis Used in Research Studies
Meaning of Correlation
Correlation is simply a statistical method of measuring the relationship between two or more variables. It is a statistical device that measures the degree of association between two or more variables.
To measure the degree of association between two or more variables like height and weight of children, capital and deposit, income and expenditure, blood pressure and age etc. one more summary statistics is needed and is known as correlation coefficient. Correlation coefficient is generally denoted by ‘r’.
Types of Correlation
Correlation can be classified into followings:
1. Positive Correlation
The direct relationship between two variables is called positive correlation. If there is relationship between two variables in such a way that when one variable’s values tend to increase, the other variable’s values also tend to increase and when one variable’s values tend to decrease, the other variable’s values also tend to decrease then such relationship between variables is known as positive correlation. Demand and supply of a product, income and expenditure of a person etc. are the best examples of positive correlation.
2. Negative Correlation
The opposite or indirect relationship between two variables is known as negative correlation. It denotes relationship between two or more variables in such a way that if one variables values tend to increase then other variable’s values tend to decrease and vice versa. Price and demand, altitude and temperature etc. are the best examples of negative correlation.
3. Linear Correlation
If the values of two variables are proportional to each other, it is called linear correlation. Under this, the values of two variables either increases or decreases in a constant ratio.
4. Non-linear Correlation
It is just opposite of linear correlation. If the values of two variables are not proportional to each other, it is called non-linear correlation. Under this, values of two variables are not in a constant ratio.
5. Partial Correlation
It is the degree of association between the dependent variable and only one particular independent variable amongst many independent variables keeping the effect of other independent variables constant. It is also called net correlation.
6. Multiple Correlation
The degree of association between three or more variables at a time, is called multiple correlation. This measures the correlation between the dependent variable and other independent variables.
3 Best Examples of Correlation
- The first example of correlation is the correlation between the number of people in a country and the number of people who are unemployed.
- The second example is about how there is a correlation between the speed at which an object falls and its weight.
- The third example is about how there is a correlation between two different types of music, classical and hip-hop.
In all three examples, we see that there are correlations between two variables. For example, in the first case, we see that as population increases, so does unemployment. In another case, we see that as weight increases, so does the speed at which an object falls to earth. Lastly, in the third case we see that as classical music becomes more popular so does hip-hop music becoming less popular.
Uses of Correlation
1. Correlation is used in research to measure how two or more variables are related to each other.
2. It’s also used in statistics and the social sciences to determine the strength of a relationship between variables, such as in linear regression and ANOVA models.
3. Correlation can be used to find hidden relationships that might not be obvious from looking at one variable alone, such as between blood pressure and cholesterol levels in patients who have had heart attacks.
4. It can be used when there are too many variables to look at all of them individually.
5. Correlation is also useful when you want to know whether different people react differently to a given stimulus.
Why is Correlation Analysis Used in Research Studies
Correlation analysis is used in research studies to find out if there is a relationship between two variables. This can be done by plotting the data on a scatter plot and finding out whether there is a linear trend.
The advantage of correlation analysis is that it can be used to explore any type of relationship between two variables, such as positive or negative. It also gives us an idea of how strong the relationship between the two variables are (how close they are on the scatter plot) and what their direction is (positive or negative).
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