Threats to Internal Validity I: History, Instrumentation & Subject Mortality

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  • 0:07 Internal Validity
  • 1:47 History
  • 3:39 Mortality
  • 5:01 Instrumentation
  • 6:19 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.

In research, there are many things besides the independent variable that can affect the dependent variable. In this lesson, we'll look at three of those things - history, mortality, and instrumentation - and what they mean to research.

Internal Validity

José is a psychologist, and he's developed a new therapy approach to treat depression. He thinks that his new approach will reduce depression in patients even better than traditional counseling can.

In order to find out, José designs a study to test his new therapy in comparison with the traditional way of treating depression. He gathers a bunch of depressed patients and measures how depressed they are at the start of the study. Then he treats half of them the traditional way and half of them with his new form of therapy.

After a while, he will measure the depression levels of the patients again and compare them to see if the patients exposed to his new treatment have seen more improvement than the patients exposed to the traditional form of counseling.

The purpose of José's study is to say that his new treatment causes more improvement in depressed patients than traditional treatment. But, what if the patients who get his treatment improve because they were going to improve anyway? What if they are better because they got a new job or found a new special someone?

Conversely, what if the people who got the traditional treatment did not improve because they got fired or went through a divorce?

In order to say that it is the treatment and only the treatment that caused the improvement, José's study must have high internal validity, or the extent to which the researcher can prove that only the independent variable caused changes in the dependent variable.

The first step to making sure that a study has high internal validity is to recognize the common threats to internal validity. Let's go through three of the major threats: history, mortality, and instrumentation.


Imagine that José treats the subjects who are getting his new treatment in August of 2001 and the subjects getting the traditional treatment during September of 2001. On the last day of each month, he measures his patients' depression to see how much they have improved. On August 31, he measures the patients who got his new treatment, and, overall, they improved quite a bit.

But then on September 30, he measures the patients who received the traditional treatment, and their depression seems even worse than it was at the beginning of September.

On paper, that looks pretty good: José has evidence that his treatment causes improvement, while the traditional treatment seems to cause depression to become worse. But when we look closer, we discover that something happened during the traditional treatment month: September 11, 2001, falls right in the middle of that month.

José is looking at the internal validity threat of history, which happens when something major occurs in the middle of the study that affects only one group. If he had measured everyone in September, it wouldn't have mattered because any depression caused by the terrorist attacks on September 11th would have been spread out among both conditions.

But, because the attacks happened after he'd already measured the group getting his new treatment, they weren't affected by it. And, he can't know if the difference is because his treatment is better or because those patients who got the traditional treatment were exposed to this historical event.

History doesn't have to be something big like September 11th. It can be something as small as doing research on subjects who are in the middle of midterms or doing research in a school classroom where they have just gotten back test scores. The trick is to make sure that it affects both conditions in the study, or you might have a problem with internal validity.


Let's say that José did not have a problem with history, though; he measured both groups at the same time, and everything seemed to be fine. But when he goes to measure everyone's depression at the end of the study, he discovers something alarming: almost half of the people in the group that got his new treatment have dropped out. Maybe they moved away, or passed away, or just decided to stop seeing José.

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