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

Hi, Anderson Smith again.

Â In most of the experiments we talked about,

Â we're dealing with one independent variable and one dependent variable.

Â There are many situations, however,

Â where we want to use more than just one independent and one dependent variable.

Â And now designs can do that.

Â In fact, they can be very rich designs giving us a lot of information about

Â what's going on.

Â For example, many times you want to have multiple dependent variables,

Â where we have different measures of the same construct.

Â For example, if we're going to look at stress, we could look at arousal,

Â how aroused we are, that's a measure of stress, cortisol level,

Â which you can measure very simply, just self-reports, how stressed are you?

Â We can look at those and that helps us, for example, one measure of

Â these things that we all know measure stress can be used as a manipulation and

Â then another one can be used to simply check with the manipulation.

Â Is the manipulation that we are making really doing what we say it's doing, and

Â that's called manipulation check.

Â And we can do that if we have multiple measures of the same construct,

Â multiple measures of the same dependent variable.

Â We also might want to just look at different

Â constructs measured in the same experiment.

Â We might want to look at stress, but also anger and worry and anxiety.

Â Other things which have different definitions than stress might covary in

Â the same way.

Â So in many situations where you might want to use multiple dependent variables.

Â Likewise in many situations where you might want to use multiple independent

Â variables, manipulate more than just one thing in the experiment.

Â And we can do that.

Â In fact, it's more efficient to measure multiple independent variables in the same

Â experiment.

Â If we have four independent variables we're interested, it's more efficient than

Â in one experiment, they have four different experiments.

Â And those are called factorial designs.

Â Designs that have factorial manipulations of many independent variables

Â at the same time.

Â And the advantage of multi-variable experiments is if you can look at the main

Â effects of each independent variable like we would do an experiment with

Â just one independent variable, but now with multiple independent variables,

Â we can also look at interactions among them.

Â That is, the effects of one independent variable might really depend

Â on one of the other independent variables.

Â And we look at those interactions between variables when we have a multivariable

Â experiment.

Â Now each level of one independent variable is paired with each level

Â of another independent variable, and that creates a condition of the experiment.

Â The way you do that, let's say we have three variables, one that has two levels,

Â another that has three levels, and a third that has two levels.

Â We want to know how many conditions we have, we simply multiply those together.

Â So two variables, two levels of one variable, times three levels of the second

Â variable, gives us 6, times two levels of the third variable, gives us 12, so that

Â 12 different conditions in the experiment that we would assign subjects to.

Â One representing each level of each variable.

Â And when we do that, then we can look at interactions

Â as well as main effects in our statistical designs.

Â Now, we do have to randomly assign participants to each condition.

Â We do that with all experimental designs.

Â But we know now how many conditions we have to have.

Â With three variables, with one with two levels, one with three levels,

Â one with two levels, we would have 12 different conditions.

Â Let's look at an example.

Â Here's a factorial design with two independent variables,

Â each having two levels.

Â So that means two times two is four different conditions.

Â We have two levels of the first independent variable, A and B, and

Â then two levels of the second independent variable little a and b.

Â So we have four conditions.

Â And we would randomly assign

Â participants to each of these four conditions in order to do the experiment.

Â And now we can look at the main effect of independent variable 1 in a statistical

Â analysis, the main effect of independent variable 2, and the interaction.

Â Does the effect of independent variable 1 or

Â independent variable 2 depend on the effect of the other independent variable,

Â that interaction sometimes gives us the most important findings in an experiment.

Â Let's give an example.

Â Let's say we want to study the effects of room temperature, independent variable 1,

Â and light intensity, independent variable 2, on student test taking.

Â Their performance on tests.

Â Very applied question, but it can be asked.

Â So we have two levels of room temperature.

Â Let's say 72 degrees and 92 degrees.

Â And then we have two levels of room illumination,

Â let's say the regular lighting in the classroom, and

Â then very bright lighting by bringing in a bunch of floodlights.

Â So we have four conditions.

Â And now we do the experiment and we find that both are important.

Â Room temperature produces a big effect, and even lighting produces effect.

Â But they have sort of different kinds of effects.

Â And that different kinds of effects is reflected in the fact that there's

Â a significant interaction.

Â That the effects of lighting depend upon the effect of room temperature.

Â Let me just plot that out to show you what I mean.

Â Remember this is a hypothetical study that we're doing here.

Â So we have regular or bright lighting, lighting level,

Â we're measuring test performance and we have two temperatures.

Â And what we found that if temperature 72, the effective of lighting is very small.

Â If the temperature is 92, the effective lighting is much more dramatic.

Â So we have bright lights when the room is hot.

Â That produces a greater decline in performance.

Â And they both are significant, both in they decline with the lighting and

Â decline with the temperature, but the interaction is the big effect here.

Â That the decline with lighting level really depends upon the temperature

Â of the room, the interaction.

Â Interactions can be absolutely the most important way to look at what the effects

Â are in the experiment.

Â Thank you.

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