This is a five-section course as part of a two-course sequence in Research Methods in Psychology. This course deals with experimental methods whereas the other course dealt with descriptive methods.

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From the course by Georgia Institute of Technology

Experimental Research Methods in Psychology

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Georgia Institute of Technology

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This is a five-section course as part of a two-course sequence in Research Methods in Psychology. This course deals with experimental methods whereas the other course dealt with descriptive methods.

From the lesson

Introduction

- Dr. Anderson D. SmithRegentsâ€™ Professor Emeritus

School of Psychology

Hello, Anderson Smith again.

Â And today we're going to talk about

Â the most important consideration when using the experimental method in psychology,

Â and that is the idea of control.

Â Now, we need control because often when we're doing an experiment,

Â we don't have the exact relationship between

Â the independent variable that we

Â manipulate and the dependent variable that we're measuring,

Â as an extraneous variable that's really correlated with both.

Â And when that's called a confounding Variable.

Â We actually talked about confounding variables

Â earlier when we talked about variables in general.

Â But we need to examine the independent variable when

Â this extraneous variable is controlled for- this confounded variable is controlled for.

Â So let's take an image of what that might look like.

Â We have an independent variable that we're manipulating.

Â We assume that it's the variable that's causing

Â the changes we measure in the dependent variable.

Â But it might be that a confounding variable,

Â some extraneous variable we don't really know about is

Â correlated with both the independent variable and the dependent variable.

Â So we need to control that,

Â we need to control that confounding variable.

Â So we really are looking at the independent variable that we want to show,

Â is influencing the dependent variable. Let's take an example.

Â Polio was a disease when I was growing up that was very prevalent.

Â I had two very close friends that had polio as children,

Â and I went to school with them.

Â And they rode in wheelchairs or they had braces,

Â or they walked with crutches.

Â In 1949, a Doctor Sandler noticed that there was

Â a correlation between the incidence of polio and ice cream consumption in children,

Â when he assumed that ice cream consumption was really sugar consumption

Â and that was really what led to the increase in the risk for polio.

Â And in fact the public health officials in those days

Â actually issued warnings about sugar consumption in children,

Â saying that they had to watch the sugar consumption because it could lead to polio.

Â But it was warm weather that increased the risk of polio,

Â not ice cream consumption.

Â In fact, polio is a virus and we know that viruses are much more active in the summer.

Â And the summer is correlated with warm weather,

Â is correlated with both ice cream consumption,

Â and the activity of the virus,

Â that's the increased risk of polio.

Â So we had this relationship that Dr. Sandler thought

Â about sugar consumption leads to increased risk of polio,

Â but we also have a variable that wasn't considered that's correlated with both.

Â Warm weather is correlated with sugar consumption and

Â warm weather is also correlated with the risk of polio,

Â it's a confounding variable.

Â And we need to worry about controlling that so we

Â can really look at the relationship we're interested in,

Â and that now we know is the warm weather in the increased risk of polio.

Â But we have to control for one of the variables that

Â might be confounding to that important relationship.

Â So if we have a confounding variable,

Â that simply means we have lack of

Â control and we had to figure out how to control for that.

Â To control for confounding variables there are several things you can do.

Â Better, you can redefine the measure of your independent variable so it's not confounded.

Â For example, we want to change the variable from sugar consumption to warm weather,

Â we are better defining what our independent variable is.

Â Second thing we can do,

Â is include the confounding variable in the design as another variable.

Â So we actually are measuring both warm weather

Â and sugar consumption in the same experiment.

Â That controls for what is the remaining variable- what is a confounding variable.

Â And another way then we can do is match.

Â We simply can match,

Â select subjects so that one variable is not really of a concern anymore.

Â And then there are statistical techniques that can be used

Â called analysis of covariance that allows us to exclude certain variables.

Â All of these are possible methods in the experimental method that we can

Â use to control for confounding variables.

Â So the first is better definitions.

Â We can better specify the measure of the independent variable so

Â it's not confounded with this confounded extraneous variable.

Â So the independent variable should be,

Â sugar consumption and not eating ice cream.

Â That's the first thing, is that ice cream per se but

Â sugar consumption and that even Dr. Sandler realized that.

Â And sugar consumption throughout the year should be the independent variable.

Â That's the variable we're looking at not just sugar consumption,

Â ice cream consumption in the summertime.

Â So if sugar consumption leads to a risk of polio,

Â we really want to test that relationship.

Â Then we should look at sugar consumption throughout the year.

Â The second method of controlling is including it as a variable,

Â actually making it a part of the experiment.

Â So if we now have these two variables that are correlated,

Â we don't know which one is the actual cause,

Â then we should be looking at both at the same time.

Â For example, if we had

Â sugar consumption in warm or cool weather then we could have all four groups.

Â We could have a group that has high sugar consumption in the summer when it's warm,

Â and high sugar consumption in the winter when it's cool.

Â We can have low sugar consumption in the summer when it's

Â warm and low sugar consumption in the winter when it's cool.

Â Now, we can look at the effect of whether it's warm weather or not,

Â and whether sugar consumption is high or low.

Â They both are variables and I can look at them

Â both and actually look at interactions that we talked about.

Â The interaction between the two variables in these

Â and making it a multi variable experiment.

Â The third method for controlling confounding variables is matching.

Â We simply match the selection of subjects and

Â their sample that have the same one level of one of the two variables.

Â So we might equate our sugar consumption and only use summer when it's warm,

Â or compare, equate subjects also in summer and in winter.

Â So we have matched on sugar consumption and not matched on sugar consumption.

Â And then we look at the risk of polio,

Â and what we'll find is that when they're matched on

Â sugar consumption or not matched in sugar consumption,

Â it doesn't really matter if we are not looking at warm and cool weather.

Â We're matching them when we select the subjects,

Â we take subjects that have exactly the same level of sugar consumption,

Â and thus, we're not looking at sugar consumption as a variable.

Â And then the last method that we can use to

Â control for confounded variable is actually make a statistical control,

Â actually looking at what's called a covariance design.

Â And in that case,

Â we are actually statistically controlling for

Â one variable when we are looking at the other,

Â when there's a correlation between the two.

Â And that's called, Analysis of Covariance or

Â ANCOVA which actually allows us to look at one variable in two or more groups,

Â take into account the variability that the other variable has,

Â that's called a covariate.

Â So we have a variable we're interested in and then

Â we look at the covariate the other variable,

Â and we take that out when we are comparing our independent variable.

Â So the risk of polio is a dependent variable and sugar consumption might be

Â the covariate that we want to take out to see

Â whether sugar consumption is really the cause.

Â Let me use Venn diagrams will show how this effect works.

Â Here we have the three variables, the independent variable,

Â sugar consumption, the dependent variable which might be risk of polio.

Â And then we have a confounding variable,

Â the weather, and they all three are related to each other.

Â There is a relationship as you can see

Â the overlap between the independent and the dependent variable,

Â but there's also an overlap with

Â both the independent variable and the dependent variable of

Â the confounding variable, the covariate.

Â So we take that out.

Â We take the covariate out statistically,

Â it leaves us only with a relationship between the independent variable,

Â the dependent variable is left over.

Â And as we can see there's a significant level of that left over.

Â So if we said that warm weather was what we took out,

Â or we'll say sugar consumption was what we took out,

Â then we still have a relationship

Â between the independent variable and dependent variable.

Â So it's not sugar consumption,

Â it has to be something else,

Â and now we know it's the weather.

Â So there are different ways you can control for confounding variables,

Â different methods that we can could use,

Â but we need to do that when we find there is a confounding variable

Â that's correlated with both the independent variable and a dependent variable. Thank you.

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