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

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Discussion Overview

The discussion centers around the interpretation of a correlation coefficient (r-value) of 0.93 in the context of social sciences, particularly psychology. Participants explore whether this value is considered strong, weak, or somewhere in between, while also addressing the implications of high correlation values in relation to causation and the replication crisis in psychology.

Discussion Character

  • Debate/contested
  • Conceptual clarification
  • Exploratory

Main Points Raised

  • Some participants suggest that an r-value of 0.93 is high and indicates that a significant portion of the variation in the dependent variable can be explained by the independent variable.
  • Others caution that a high r-value does not necessarily imply a causal relationship, as similar trends can occur without a direct connection between the variables.
  • A participant mentions that the r-squared value (r²) is also important, noting that it represents the proportion of variance explained, which in this case would be 0.86.
  • There is a reference to a correlation between unrelated variables, such as honey bee colonies and marriage rates, to illustrate the potential pitfalls of interpreting high correlation values.
  • One participant raises concerns about the validity of the data, questioning whether it is real or fabricated for illustrative purposes.
  • Another participant highlights the ongoing replication crisis in psychology, suggesting that findings should be treated with skepticism if replication attempts have not been made.

Areas of Agreement / Disagreement

Participants do not reach a consensus on whether an r-value of 0.93 is definitively strong or weak in social sciences. There are competing views on the implications of high correlation values and the importance of considering the context and potential for spurious correlations.

Contextual Notes

Participants express uncertainty regarding the reliability of the data and the implications of high correlation coefficients, particularly in light of the replication crisis in psychology. There is also a discussion about the potential for misleading interpretations of correlation without establishing causation.

qspeechc
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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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