Correlational research is a quantitative method of research which aims to determine if there exists any kind of relationship between two variables. Correlational research is an easy way to understand how two or more groups are related to each other.
For example, a correlational study on cigarette smoking can examine the relationship between cigarette smoking and lung cancer.
The research method is purely observational in nature and the researcher merely examines the variables without any kind of manipulation. It is not to be confused with the cross sectional research as the nature and the scope of both these studies is entirely different.
To determine the relationship between two variables, a correlational coefficient is used, which is denoted by r. The range of correlational coefficient is between 1 to +1. The value tells us two things about the nature of the relationship between two or more variables, the intensity and the direction.
Ideally, for no correlation between two variables, the value of r should be 0 and for a perfect correlation, the value of r should be 1. These are very rare scenarios and ideally, if the value of r is above 0.70, the relationship is considered to be 'almost always significant'.
Direction signifies the manner in which the two variables move in respect to each other. Mentioned here are three types of correlation.
People who have attained higher education like MBA, doctorate degrees, post-graduation tend to earn higher salaries.
✜ A negative correlation on the other hand implies that the two variables move in opposite directions. For example, when you increase the speed of your car, the time taken to reach your destination decreases.
✜ No correlation indicates that there is no relation between the two variables. For example, consumption of burgers and sales of the latest smartphone. In this scenario, these two events are mutually exclusive of each other, i.e. the occurrence of one does not in any way impact or effect the occurrence of other.
Out of the various designs, explanatory design model and prediction design model are widely used. The explanatory design examines the correlation of two and more variables with data being collected at one time only. After the collection of data, at least two scores are recorded and the researcher draws out inferences from the available statistics only.
On the other hand, in a prediction design model, the capability of the prediction is the main aim of the research. The study focuses on the use of predictor variable and the criterion variable.
A variable which is used to predict the value of the other variable is known as the predictor variable and the variable whose value is being predicted is known as the criterion variable. The prediction design is most useful for forecasting academic success.
Correlation and Causality
One of the most common mistakes associated with a correlational research is the interpretation of a correlation as a causality. A correlational research can only analyze the relationship between two variables, but it does not tell us about the cause and effect relationship.
If two variables are negatively correlated to each other, it does not necessarily mean that they have a cause and effect relationship. Consider this example.
Suppose a study on the students of a college and their average grades found out that students who had undergone body-piercing had lower grades. So, does this mean that getting pierced affects one's intelligence and makes a person dull?
Should the college ban all kinds of body piercing in order to improve its score, and would restrictions on body piercing ensure higher scores? In this case, there is a third variable that affects the grades of the students.
The third variable can be any reason like a difficult childhood, use of drugs etc. So, one should never interpret the findings of a correlational research as a causality.
Correlational research is one of the widely used methods of research. It ensures that the researcher just reports the data without making any changes to the behavior of the participant.
Also, it does not require much capital and time like several other researches do. But, its main drawback is that it is purely analytical in nature and often is misinterpreted as causality.