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

Threats to Internal Validity

- Dr. Anderson D. SmithRegentsâ€™ Professor Emeritus

School of Psychology

Hi, Anderson Smith and we're talking about threats to internal validity.

Â And last time, we talked about maturation and history.

Â Things that can change an environment and

Â people that could threaten the internal validity, the relationship that we're

Â trying to find between an independent variable and independent variable.

Â And now, I want to talk about regression.

Â A statistical concept that's very important in looking at differences.

Â There's a tendency for

Â extreme measures to be closer to the mean on subsequent measurements.

Â It's just a statistical happening.

Â It's going to happen.

Â As if I have an extreme measure on trial one, then it's going to be closer to

Â the mean on trial two in dependent on whether raise on manipulate.

Â If you have a somewhat non-random sample,

Â you also get increase regression to the mean.

Â And if I have possibility of having more extreme scores,

Â I will have increase regression to the mean.

Â So for example, if we look, this is actual study that was done in 2007.

Â We're looking batting averages between 2005 and 2006.

Â And notice that if you have an extreme batting average,

Â it tends to be closer to the mean the year later.

Â So, these four batting averages are actually closer

Â to the mean simply due to statistics.

Â It's just going to happen, because the probability of being closer to the mean is

Â always going to be greater than being further away from the mean.

Â Because you're already at an extreme score.

Â So, regression to the mean.

Â It was first discussed by Francis Galton who was knighted in the 1980s and

Â he was probably one of the early statisticians.

Â He actually was the first to talk about correlations, for example.

Â And he discovered, just to give you an example that the correlation between

Â the father's height and the son's height was 0.50.

Â So there's a correlation with the height of the father and

Â the height of the son, and that's about 0.50.

Â And he assumed that 50% was what he called the heritability level,

Â the probability that there's a relationship between the height.

Â But he also talked about regression to the mean.

Â The higher you are in height or the lower you are in height,

Â the changes are going to be greater.

Â Because of simply regression to the mean.

Â For example, he found that his father's height was one standard deviation below

Â average, then the prediction of the height of the son would be 50% times

Â 1 of the standard deviation below average.

Â And the further away it is, the lower is,

Â then the greater that difference is going to be.

Â So the difference is going to be smaller, depending how extreme the scores are.

Â If the father's height was two standard deviations above the average,

Â that is a more extreme score, then the son's height was predicted to be 0.50x2 or

Â 1 standard deviation above average.

Â So the bigger the difference,

Â depends upon the original level of one of the variables.

Â So tall children tend to have tall children, but not all that tall.

Â Because of regression to the mean.

Â One of the things he studied, Sir Francis Galton was intelligence and

Â he believed that it wasn't regression to the mean.

Â It was regression to mediocrity.

Â That is the kids tend to be more like the mean and

Â plus very smart parents, less smart kids.

Â Regression to the mediocrity.

Â So, the tendency for extreme measures to be closer to the mean on subsequent

Â measurements is what we mean by that.

Â And the reasons are that if we have extreme measures,

Â the probability of getting a more extreme measure simply is small.

Â It's a statistical artifact and that we need to be closer to the mean,

Â because that's the only direction that it can go in or

Â that's the primary direction it can go in.

Â A little example of everyday life.

Â If you pick a card and it's the queen,

Â the probability that your partner will pick a card that's higher is very small.

Â The probability of picking a card that's lower is great,

Â simply because there are not mini cards higher than a queen.

Â That mini card's lower than the queen.

Â So, regression to the mean likely to occur.

Â So average goes up or down, depending upon what the extreme score is and

Â this is just an example.

Â Looking at three point shooting by half and some ball games, basketball games.

Â And overall, you can see scores that are going up.

Â Scores are going down.

Â But overall, you can see that it's regression to the mean.

Â The mean is here and the variance around the mean is smaller here is out there.

Â Regression to the mean.

Â Having nothing to do with how well they are basketball players and

Â this is another showing the same thing.

Â A regression to the mean, score on day one, score on day two and while there

Â are some extreme scores that really are related to whatever it is we're measuring.

Â Overall, the higher your score on day one, the lower your score on day two.

Â And the higher you'll score on day one, I should say,

Â the higher the score on fay one, the lower the score on day two.

Â So, you get this regression to the mean.

Â Sometimes, it's called the football effect distribution.

Â The other thing is when you have a trend that looks like this line.

Â In fact, the scores sort of vary around the line in this sort of circular,

Â cyclic kind of way.

Â Because you have not only the increase in the trend of the measure over time, but

Â you also have regression of the mean that's occurring.

Â So for example, here's the actual data from incidence rate of childhood I

Â diabetes in Western Australia and

Â you can see that the probability incidence rate of diabetes is going up with time.

Â But you also see the actual data show these cyclic variations

Â in the scores and that's a collection of both the average.

Â That is the trend which is what we're probably interested in doing and

Â studying this particular phenomenon, but it also shows a regression to the mean.

Â The fact that if you get above the mean greatly, it tends to regress to the mean.

Â It shoots below that and

Â then it's sort of constantly regressing to the mean over time.

Â Thank you.

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