What is Correlational Research? Definition, Types, Example, Pros, and Cons

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What is Correlational Research?

The research in which the relationship between two sets of variables is examined to determine whether they are associated or correlated is called correlational research.

To understand the meaning of correlation research, at the beginning it is necessary to understand the meaning of variables. In psychology, variables are behaviors, events, or other characteristics that can change, or vary in some way.

For example, in a study to determine whether psychological understanding makes a difference in managerial behavior, the variables would be psychological understanding and managerial behavior. The former psychological understanding is called independent and later the managerial behavior is called the dependent variable.

The correlation between two variables is shown through the correlation coefficient where the value is measured between -1 and +1. When the correlation coefficient is close to +1 then there is a positive correlation between the two variables. When the value is close to -1, then there is a negative correlation between the two variables and when the value is close to zero there is no relationship between the two variables.

Example

Let us take an example to understand correlational research. Consider hypothetically, a researcher is studying a correlation between psychological understanding and managerial behavior. In this study, there are two variables psychological understanding and managerial behavior. Let us say managerial behavior has a positive correlation with psychological understanding. This means the managers who have psychological understanding are likely to have good managerial behavior.

Types Correlational Research

Essentially, there are three types of correlational research:

1. Positive Correlation

A positive correlation between two variables is when an increase in one variable leads to an increase in the other variable and a decrease in one variable will see a decrease in another variable. For example, the amount of money a person might have positively correlated with the number of cars she/he has.

2. Negative Correlation

A negative correlation is quite literally the opposite of a positive correlation. This means, if there is an increase in one variable, the second variable will show a decrease and vice versa. For example, as more employees are laid off, satisfaction among remaining employees decreases.

3. No Correlation

No correlation means two variables are not correlated to each other. This means a change in one variable may not necessarily see a change in the other variable. For example, being a millionaire and happiness are not correlated. This means an increase in money does not lead to happiness.