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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?
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
Used in marketing, your correlational research may help you identify a new potential target market. For example, if you observe shoppers at a local grocery for an entire week, you might conclude that older shoppers tend to visit the store early in the morning. This relationship between time of day and customer age will help you target your advertising appropriately.
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