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Analyzing Effects to Determine Correlation or Causation

Analyzing Effects to Determine Correlation or Causation
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  • 0:00 Correlation vs. Causation
  • 0:25 Temporal Precedence
  • 1:43 Covariance &…
  • 2:42 Independent Variable Testing
  • 4:18 Lesson Summary
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Instructor: Matt Lamb

Matt has tutored for six years now, in a variety of subjects including reading, essay writing, chemistry, and theology. He is finishing his M.A. in Political Science this August.

Correlation is when two events happen together, and causation is when one event causes the second event. In this lesson, we will look at how to determine when effects are examples of correlation or causation.

Correlation vs. Causation

Determining when an event is an example of correlation or causation can get confusing. To begin, remember that correlation is when two events happen together, but causation is when one event causes the second event. By the end of the lesson, you'll be able to do a series of tests to figure out whether there is a correlation or a causation.

Temporal Precedence

When analyzing an effect to determine correlation or causation, the first thing you need to do is consider temporal precedence, which is a process that establishes what comes first: the cause or the effect. The most classic example of this is the question: what comes first, the chicken or the egg? The harder it is to determine what happens first the weaker the relationship of causality. For example, if you immediately feel sore after every time you lift weights, there's a good chance that lifting weights causes you to be sore. However, this is not always going to be clear. For example, let's say that people who drink alcohol have higher rates of depression. Does depression lead to drinking alcohol as a method of coping? Or do people who drink alcohol develop depression because of the way alcohol interacts with the brain? Because depression and alcoholism can happen over long periods of time, it can be difficult to determine which comes first.

Another example is the association between happiness and relationship status. Are happy people more likely to be in a dating or marriage relationship? Do people start dating someone and then the companionship makes them happier? Or does happiness come before dating? Perhaps people are attracted to people who seem happy, because they project a good life that other people want to be part of.

Covariance & Confounding Variables

Establishing a relationship is an important step. Before figuring out if a relationship is correlation or causation, we first need to determine the covariance of cause and effect, which is the strength of the relationship. For example, we might find that people who frequently lift weights tend to have higher muscle mass than people who don't frequently lift weights. While we need to do further investigation to determine if lifting weights causes higher muscle mass or if people who are born strong gravitate to lifting weights, we at least can determine a general pattern between the two. However, we might find that there is not a strong relationship at all. If a person who regularly lifts weights is no more likely to have higher muscle mass than someone who does not lift weights, we would say there is not a relationship present.

There can also be a confounding variable, which can be another reason for the relationship. For example, perhaps great athletes are great because they're strong, and then they continue to lift weights to become better athletes.

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