If I have a significant correlation coefficient of r=.80 between Variable A and B, I can work out the effect size (coefficient of determination) by squaring it, which is 64%.
I want to graph this in the simplest way possibe (given my non-statistical target audience). Can I use a 100% stacked bar graph for this purpose. This will show Variable B as 100% and on top of it would be Variable A which would be 64%. I can then graphically say that 64% of variance in Variable B can be attributed to Variable A. (Conversely, I can also graphically say that I am unsure of the remaining 36% of Variable B)
The appeal of this approach for me is that I can show the effect size between A and B on a number of variables (e.g. gender, age, education) in one graph. This will also make a colourful presentation (which is good for a non statistical target audience!).
I have seen some textbooks showing two circles, each representing a variable (e.g. A and B). The part of the circles that overlaps illustrates the effect size. I thought doing a 100% stacked bar graph was a better way.
From the discussion below, it appears that the scatterplot is the way to go on this matter. However, how do I show 64% on a scatterplot?
I think the point is being missed in the discussion below. It is easy to illustrate the relationship through the scatterplot but how is the effect size illustrated i.e. the actual perecentage as above. I can't see this percentage figure in any of the diagrams below.
I agree with the person who suggested a scatterplot. An R-square gives the proportion of variance statistically explained by A. It doesn't give the effect size in the way I think you mean. If you just want to illustrate a proportion, any figure would do, but very little information is conveyed. The "effect" of the explanatory variable is shown in a scatterplot by the closeness of the points to a regression line (or one drawn "by hand" if need be).
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