What is Internal Validity in Research? - Definition & Examples

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  • 0:06 Internal Validity
  • 1:31 Importance
  • 2:43 Threats
  • 5:32 Lesson Summary
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Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

The purpose of most research is to show that one variable causes changes in another variable. But, what happens when other variables come into play? In this lesson, we'll explore the definition, importance and threats to internal validity.

Internal Validity

Sean works for a large corporation, and they've hired someone to figure out if more money will mean more productivity for their workforce. In other words, they want to know if they pay Sean a higher salary, will he work more?

At first glance, the answer appears to be yes. After all, the people who get paid the most at the company tend to be the ones that come in early and stay late. They are the hardest working people in the company. So, it stands to reason that the more a person gets paid, the harder they will work, right?

Maybe, but it's actually a bit more complicated than that. Maybe those people get paid the most because they were already hard workers. Maybe they're motivated to work hard because they really like what they do and the pay is incidental. Maybe they are hyper competitive and don't want to be the first to leave the office.

How do we know what the cause of their hard work is? In research, internal validity is the extent to which you are able to say that no other variables except the one you're studying caused the result. For example, if we are studying the variable of pay and the result of hard work, we want to be able to say that no other reason (not personality, not motivation, not competition) causes the hard work. We want to say that pay and pay alone makes people like Sean work harder.


You may be wondering why we should care about internal validity. If people who work the hardest get paid the most, then why not just say that's what happens and call it a day?

The purpose of most research is to study how one thing (called the independent variable) affects another (called the dependent variable). The strongest statement in research is one of causality. That is, if we can say that the independent variable causes the dependent variable, we have made the strongest statement there is in research.

But, that's not possible if an experiment has low internal validity. Remember our example from above? How do we know that pay causes harder work if there are other possibilities, like competition or motivation? The answer is that we don't. That's why internal validity is so important.

The best experiments are designed to try to eliminate the possibility that anything other than the independent variable caused the changes in the dependent variable. In our experiment, we would try to eliminate all other things that might be causing the hard work by the workers. If we can do that, then we can show that higher pay causes harder work.


But, designing a study that allows you to prove causality isn't as easy as it might seem. That's because there are several common threats to internal validity. These are things that make it difficult to prove that the independent variable is causing the changes in the dependent variable.

One threat to internal validity is selection. This is simply the fact that the people who are studied may not be normal. Do the people at Sean's company who get paid the most work hard because they are paid a lot, or do they get paid a lot because they are inherently hard workers? By studying them, we might be studying just people who already work hard; we have accidentally selected people whose experience does not mirror everyone else's.

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