Return to Question

I am looking at whether abundance is related to size. Size is (of course) continuous, however, abundance is recorded on a scale such that

A = 0-10
B = 11-25
C = 26-50
D = 51-100
E = 101-250
F = 251-500
G = 501-1000
H = 1001-2500
I = 2501-5000
J = 5001-10,000
etc... 

A through Q... 17 levels. I was thinking one possible approach would be to assign each letter a number: either the minimum, maximum, or median (ie A=5, B=18, C=38, D=75.5...).

What are the potential pitfalls -- and as such, would it better to treat this data as categorical?

I have read through this question which provides some thoughts -- but one of the keys of this data set is that the categories are not even -- so treating it as categorical would assume the difference between A and B is the same as the difference between B and C... (which can be rectified by using logarithm - thanks Anonymouse)

Ultimately, I would like to see whether size can be used as a predictor for abundance after taking other environmental factors into consideration. The prediction will also be in a range: Given size X and factors A, B, and C we predict that Abundance Y will fall between Min and Max (which I suppose could span one or more scale points: More than Min D and less than Max F... though the more precise the better).

I am looking at whether abundance is related to size. Size is (of course) continuous, however, abundance is recorded on a scale such that

A = 0-10
B = 11-25
C = 26-50
D = 51-100
E = 101-250
F = 251-500
G = 501-1000
H = 1001-2500
I = 2501-5000
J = 5001-10,000
etc... 

A through Q... 17 levels. I was thinking one possible approach would be to assign each letter a number: either the minimum, maximum, or median (ie A=5, B=18, C=38, D=75.5...).

What are the potential pitfalls -- and as such, would it better to treat this data as categorical?

I have read through this question which provides some thoughts -- but one of the keys of this data set is that the categories are not even -- so treating it as categorical would assume the difference between A and B is the same as the difference between B and C... (which can be rectified by using logarithm - thanks Anonymouse)

Ultimately, I would like to see whether size can be used as a predictor for abundance after taking other environmental factors into consideration. The prediction will also be in a range: Given size X and factors A, B, and C we predict that Abundance Y will fall between Min and Max (which I suppose could span one or more scale points: More than Min D and less than Max F... though the more precise the better).

I am looking at whether abundance is related to size. Size is (of course) continuous, however, abundance is recorded on a scale such that

A = 0-10
B = 11-25
C = 26-50
D = 51-100
E = 101-250
F = 251-500
G = 501-1000
H = 1001-2500
I = 2501-5000
J = 5001-10,000
etc... 

A through Q... 17 levels. I was thinking one possible approach would be to assign each letter a number: either the minimum, maximum, or median (ie A=5, B=18, C=38, D=75.5...).

What are the potential pitfalls -- and as such, would it better to treat this data as categorical?

I have read through this question which provides some thoughts -- but one of the keys of this data set is that the categories are not even -- so treating it as categorical would assume the difference between A and B is the same as the difference between B and C... (which can be rectified by using logarithm - thanks Anonymouse)

Ultimately, I would like to see whether size can be used as a predictor for abundance after taking other environmental factors into consideration. The prediction will also be in a range: Given size X and factors A, B, and C we predict that Abundance Y will fall between Min and Max (which I suppose could span one or more scale points: More than Min D and less than Max F... though the more precise the better).

Thanks.

I am looking at whether abundance is related to size. Size is (of course) continuous, however, abundance is recorded on a scale such that

A = 0-10
B = 11-25
C = 26-50
D = 51-100
E = 101-250
F = 251-500
G = 501-1000
H = 1001-2500
I = 2501-5000
J = 5001-10,000
etc... 

A through Q... 17 levels. I was thinking one possible approach would be to assign each letter a number: either the minimum, maximum, or median (ie A=5, B=18, C=38, D=75.5...).

What are the potential pitfalls -- and as such, would it better to treat this data as categorical?

I have read through this question which provides some thoughts -- but one of the keys of this data set is that the categories are not even -- so treating it as categorical would assume the difference between A and B is the same as the difference between B and C... (which can be rectified by using logarithm - thanks Anonymouse)

Ultimately, I would like to see whether size can be used as a predictor for abundance after taking other environmental factors into consideration. The prediction will also be in a range: Given size X and factors A, B, and C we predict that Abundance Y will fall between Min and Max (which I suppose could span one or more scale points: More than Min D and less than Max F... though the more precise the better).