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How to use correlational research to spot patterns and trends

Correlational research can show if there’s a relationship between two variables. Survey studies can confirm your research.

You may be more familiar with correlational research than you realize. For example, when the doorbell rings at a particular time of day, you know it’s the mailman dropping off a package. You came to the conclusion that there is a relationship between the doorbell and the mailman at a particular time of day after observing the doorbell and the mailman, two variables, over time. This is essentially correlational research.

Let’s look more closely at what correlational research is and how you can use it to spot patterns and trends.

As we alluded to in our mailman example, correlational research is a non-experimental research method in which two variables are observed in order to establish a statistically corresponding relationship between them. The goal of correlational research is to identify variables that have a relationship in which a change in one creates a change in the other—without influence from any extraneous variable.

Correlational research has, for example, identified a relationship between watching violent television and aggressive behaviors. But we must remember that correlational is not the same as causal. To prove that viewing violent shows on television causes aggression, experimental studies were needed. Correlational research established that there was a relationship, but experimental research was needed to prove the type of relationship.

Correlational research is one of several types of research design. So, what are the key characteristics of correlational research? 

  1. Non-experimental: in correlational research, there is no manipulation of variables with a set methodology to prove a hypothesis. It is a simple observation and measurement of the natural relationship between two variables without the interference of any other variables.
  2. Backward-looking: the future is not a consideration in correlational research. It observes and measures the historical relationship between two variables. The statistical pattern is backward-looking and can cease to exist in the future. The relationship between the variables may be revealed as positive in the past but can change to negative or zero in the future.
  3. Dynamic: the statistical patterns from correlational research are dynamic. The correlation can change on a daily basis, so it cannot be used as a standard variable for further research and analysis. Two variables could have a positive correlation in the past and a negative correlation relationship in the future.

There are several benefits to conducting a correlational research study:

Variable management 

There is no need to set up a controlled environment or staged interaction. In correlational research, you simply observe the two variables, their natural relationship, and their effects on each other. Observation takes place in the natural environment of the variables, and neither variable is manipulated.

Data collection

Correlational research generally involves two or more sets of data. By conducting correlational studies over time, you can observe patterns and trends that establish further relationship attributes. Data can either be collected by observation or archival data, which we will discuss in more detail later in this article.

Target market identification