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Correlational Research: Definition, Purpose & Examples

Correlational Research: Definition, Purpose & Examples
Coming up next: The Relationship Between Variables: Correlation Coefficient & Scatterplots

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  • 0:05 Definition
  • 3:00 Example 1
  • 4:22 Example 2
  • 5:18 Lesson Summary
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Lesson Transcript
Instructor: Devin Kowalczyk

Devin has taught psychology and has a master's degree in clinical forensic psychology. He is working on his PhD.

This lesson explores, with the help of two examples, the basic idea of what a correlation is, the general purpose of using correlational research, and how a researcher might use it in a study.

Definitions in Correlational Research

Your brain can do some really cool things. For instance, you learn that a particular jingle means the ice cream trucks are nearby. The louder the jingle, the closer it is. And if you were lucky enough to have several types of ice cream trucks, you will recognize which jingle goes with which ice cream truck.

The world is full of things where if thing A happens, then there is a good chance that thing B will happen. If thing A is the jingle, then there is a good chance that thing B, the ice cream truck, is close by. We can also make things more complicated by thing A being the loudness of the jingle and thing B being the distance to the ice cream truck. As the loudness increases, the distance shrinks. As the distance increases, the loudness goes down.

This is kind of a silly example, but it's an example of how you naturally correlate one event with another. A correlation is simply defined as a relationship between two variables. The whole purpose of using correlations in research is to figure out which variables are connected. I'm also going to start referring to the things as variables; it's a more scientific name. This simple definition is the basis of several statistical tests that result in a correlation coefficient, defined as a numerical representation of the strength and direction of a relationship.

Correlation research is looking for variables that seem to interact with each other, so that when you can see one changing, you have an idea of how the other will change. This often entails the researcher using variables that they can't control. For example, a researcher may be interested in studying the preference for ice cream based on age. If we cannot assign age, does that mean we have to scrap the whole correlation? Nope!

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