Limitations of Correlational Research

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  • 0:07 Correlation
  • 1:26 Causation
  • 2:27 Directionality
  • 3:21 Third Variable
  • 4:30 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.

Many researchers use correlations in their studies, but it is not without risk. This lesson explores some of the issues that researchers may experience if they use correlations.

Correlation

One of the best statistical tests out there, in my opinion, is the correlation. Correlation is a mutual relationship between two variables. This means that when one variable goes up, the other will respond by increasing (known as a positive correlation) or decreasing (known as a negative correlation). Easy-to-understand variables include:

  • The more one studies, the higher their grades are.
  • The more one eats, the less hungry one feels.

But in the social sciences, there is rarely a perfect correlation. A perfect correlation means simultaneous and equivalent changes are seen when a variable is altered. For instance, if you were doing an experiment on studying and you saw a perfect correlation between hours spent studying and grades on tests, then you would look at your data again.

Behaviors and real life are so complicated that you rarely see a perfect correlation. There will be something running interference. You, as a researcher, want to try and reduce the amount of interference.

There are other limits to correlation, and the researcher who uses correlation techniques needs to be aware of some of the limits of correlations. And the real scary part is that they're not always as obvious as the perfect correlation issue.

Causation

Correlation does not equal causation.

Experiments are typically designed to demonstrate that variable A causes something in variable B. That's exactly what our previous examples of studying and eating are all about. However, those examples are incredibly simplistic. Here is a question that has a little more complexity: Does violent television cause people to be more violent?

The question starts to flip back and forth the more you think about it. Does violent TV make people more violent, or do violent people watch more violent TV?

A problem with correlation is that the variables you are interested in are merely interacting with each other. They are not necessarily causing one another. So whenever you are using a correlation, it is inaccurate to say variable A causes variable B. All you can say with a correlation is that variable A interacts with variable B.

Directionality

Correlations do not indicate direction of interaction.

A correlation does not tell you how variable A and variable B interact. All it tells you is that they do interact. Here is another example: As drug use increases, interpersonal relationship problems increase.

This means that as drug use increases, relationship problems also increase. Or is it that as relationship problems increase, there is also an increase in drug use? All that we can tell you from our correlation is that the two are connected.

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