I Is an R-Value of 0.93 Considered Strong in Social Sciences?

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An R-value of 0.93 is considered high in social sciences, indicating that a significant portion of the variation in one variable can be explained by another. However, caution is advised when interpreting high correlation values, as they can sometimes arise from coincidental trends rather than true relationships. The discussion highlights examples of spurious correlations, where unrelated variables show high correlation, emphasizing the need for careful analysis. The importance of the R-squared value is also noted, as it quantifies the explained variation. Overall, while a high R-value suggests a strong correlation, it does not guarantee a causal relationship.
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Hello everyone.

I stumbled across an article in the social sciences that had a correlation coefficient of r=0.93.

Being from a maths background and knowing nothing about things like social sciences, psychology, etc., is this r-value in these types of fields considered fairly strong, strong, weak...? Or what?

Cheers
 
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If you have two variables, x, and y, with a correlation of r, that means that r^2 of the variation in y can be explained by the variation in x. For example, height and weight have an r around 0.8 or so.
 
Haha, I know what the correlation coefficient means, I'm asking in the field of social sciences, psychology etc., is r=0.93 considered high? Mediocre? What?
 
It's high. It means that most of the variation of the y values can be explained by the regression equation. The fraction of variation that is explained is r2 = 0.86.

You should be judicious when drawing conclusions about relationships with a high r value. Two variables can have very similar trends even if there is no connection between them. The population of the Earth and the age of the Solar system both increase, but that does not mean that they are related.

I think it is reasonable to say that variation of y which looks random, but can be largely explained by variation of x is more likely to indicate a connection between the two variables.
 
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That's roughly the same correlation as the number of honey bee colonies in the US and the marriage rate in Vermont.

honey-producing-bee-colonies-us_marriage-rate-in-vermont.png
 
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Ah, so the r2 value is more important?
 
Vanadium 50 said:
That's roughly the same correlation as the number of honey bee colonies in the US and the marriage rate in Vermont.

View attachment 211777
This is a good example of two variables with general downward trends giving a high r2 regression value. But it also shows a periodic component that looks synchronized, driving the r2 value even higher. This is where the scientist must be judicious in his conclusions. If he searched the world for something to match to the bee hive numbers, then he is bound to find something. If he had some a priori reason to relate bee hive numbers to the marriage rate, then this is some supporting information. (I can't imagine any a priori reason.)

PS. I wonder if the data shown is real or is fictitious to make a point?
 
FactChecker said:
I wonder if the data shown is real or is fictitious to make a point?

As far as I know, it is real. It is from the "Spurious Correlations" page at www.tylervigen.com. It has other great ones - "Divorce rate in Maine" and "Per capita consumption of margarine" at r = .9926.
 
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If it's in psychology, and there haven't been replication attempts yet, I'd ignore it. There's something of a replication crisis still brewing out there.
 
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