External Validity

In this video I explain external validity (or ecological validity) and criticisms of artificiality that can arise from laboratory experimentation. I also explain naturalistic observation and field experiments as ways of overcoming artificiality and collecting real-life data, though both of these approaches have the disadvantage of sacrificing control over potential confounding variables.

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Video transcript:

Hi, I’m Michael Corayer and this is Psych Exam Review and in this video I’m going to talk about external validity or ecological validity.

Now in the last video I talked about internal validity and this was the idea that we’ve done everything correctly inside the lab setting. We’ve appropriately defined, manipulated, and measured our variables and we found some effect.

So what external validity asks is, even if we found an effect, does this effect apply to real life? So it asks whether we’ve defined our variables in a normal or typical way that people might actually experience in real life.

If not, then our study might be criticized as having artificiality. This means that even if we found a real effect, it’s an artificial effect, it doesn’t actually tell us anything about real life behavior.

So for instance if I did a study where I manipulated some level of frustration that my participants experienced and then I measured their level of aggression on some computer simulation. Well even if I find a strong effect, the question might be is will this really predict actual aggressive behavior in real life? Maybe people are free to be more aggressive because they know it’s just a simulation, it’s a game on the computer that they’re playing and in real life when there’s more consequences, they might not behave more aggressively. And so even though I have an effect, it might be an artificial effect.

So how do we get around this problem? Well, one way is to do what’s called naturalistic observation. As this name implies, this involves observing natural behaviors. So we observe real life behavior and that’s where we collect our data.

So for instance, if I want to know about people’s shopping behavior I might simply go to a store and observe. What are people actually buying? How many people are buying it? And that would tell me about shopping behavior. The problem that we have, just like with correlational studies, is that I’m only measuring, so I don’t have any evidence about what’s causing people to purchase a particular product.

So instead of just naturalistic observation, sometimes I might want to engage in what’s called a field experiment. And the difference here is that a field experiment is an experiment. It involves the manipulation of a variable. So we manipulate some variable in the environment and then we observe people’s responses to this manipulation.

So for instance, if I’m studying shopping behavior I might decide to manipulate something like whether some shoppers get a free sample or not or I might manipulate having a different type of advertising display in some stores and not others and then I can see, does this manipulation influence what people actually purchase? So I manipulate something in the environment and then I observe people’s responses to it.

That would be a field experiment. Now in both of these cases, naturalistic observation and field experiments, we’re sacrificing control so they both give us greater external validity; we’re less likely be accused of artificiality in the effects that we measure but in exchange we have to give up some of our control.

We don’t have the ability to control certain variables in these studies. So for instance if I’m measuring shopping behavior it might be the case that people who shop at this particular store differ in some important way and I can’t manipulate this, I can’t control who shops at this store and who doesn’t. So if I’m only measuring people that are shopping here there might be some difference about these people, there might be some other variable that’s influencing my results and I don’t have any way to control for this.

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Thanks for watching!

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