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Nick Cox
Nick Cox
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I don't see any special value to averaging coefficients of variation (CVs).

Ideally your CV is consistent across datasets or groups or variables in which case you can underline that fact by citing a narrow range.

Conversely, if your CV is not at all consistent an average is not informative and how to get it a secondary issue. A wider issue is that the CV is a ratio, SD/mean, and ratios are often volatile and not easily or comfortably averaged. Otherwise put, the point you make that average CVs could be calculated in different ways shows up that the notion of average CV is not well defined.

In the great majority of cases where I have seen coefficient of variation used:

  1. The implication is that you should be working on logarithmic scale.

  2. ItOr it is not really helping.

Sample data that is real or realistic for you would allow a more specific answer.

In short, getting very different results is likely to be a sign that the data need closer analysis. Ignoring the difference by averaging is going in the wrong direction.

I don't see any special value to averaging coefficients of variation (CVs).

Ideally your CV is consistent across datasets or groups or variables in which case you can underline that fact by citing a narrow range.

Conversely, if your CV is not at all consistent an average is not informative and how to get it a secondary issue. A wider issue is that the CV is a ratio, SD/mean, and ratios are often volatile and not easily or comfortably averaged. Otherwise put, the point you make that average CVs could be calculated in different ways shows up that the notion of average CV is not well defined.

In the great majority of cases where I have seen coefficient of variation used:

  1. The implication is that you should be working on logarithmic scale.

  2. It is not really helping.

Sample data that is real or realistic for you would allow a more specific answer.

In short, getting very different results is likely to be a sign that the data need closer analysis. Ignoring the difference by averaging is going in the wrong direction.

I don't see any special value to averaging coefficients of variation (CVs).

Ideally your CV is consistent across datasets or groups or variables in which case you can underline that fact by citing a narrow range.

Conversely, if your CV is not at all consistent an average is not informative and how to get it a secondary issue. A wider issue is that the CV is a ratio, SD/mean, and ratios are often volatile and not easily or comfortably averaged. Otherwise put, the point you make that average CVs could be calculated in different ways shows up that the notion of average CV is not well defined.

In the great majority of cases where I have seen coefficient of variation used:

  1. The implication is that you should be working on logarithmic scale.

  2. Or it is not really helping.

Sample data that is real or realistic for you would allow a more specific answer.

In short, getting very different results is likely to be a sign that the data need closer analysis. Ignoring the difference by averaging is going in the wrong direction.

Source Link
Nick Cox
Nick Cox
  • 59.5k
  • 8
  • 136
  • 212

I don't see any special value to averaging coefficients of variation (CVs).

Ideally your CV is consistent across datasets or groups or variables in which case you can underline that fact by citing a narrow range.

Conversely, if your CV is not at all consistent an average is not informative and how to get it a secondary issue. A wider issue is that the CV is a ratio, SD/mean, and ratios are often volatile and not easily or comfortably averaged. Otherwise put, the point you make that average CVs could be calculated in different ways shows up that the notion of average CV is not well defined.

In the great majority of cases where I have seen coefficient of variation used:

  1. The implication is that you should be working on logarithmic scale.

  2. It is not really helping.

Sample data that is real or realistic for you would allow a more specific answer.

In short, getting very different results is likely to be a sign that the data need closer analysis. Ignoring the difference by averaging is going in the wrong direction.