Significance of normalization
Normalization is a process to transform data in different units into a common scale and comparable units.
Why normalize in AHP:
The first step in AHP is to assign numbers as per the Satty's scale to different attributes which affect the decesion making.
These numbers are already dimensionless what then is the mathematics behind forming normalized matrix.?
Weightage of different attributes can be calculated from step1 itself without normalizing.
They are indeed normalized after the EV process when the sum of the values must add 1
In all other methods normalization is needed to work with unitless values, otherwise, operations would be unrealistic
Suggest reviewing this paper for MCDM methods:
https://www.sciencedirect.com/science/article/abs/pii/S092583881732827X
It depends on your data. If you have ordinal data, in most cases, there is no need to normalize data. If you have cardinal data, still there are some methods which do not need normalization. Check some methods such as Ordinal Priority Approach (Ordinal) and QUALIFLEX (Ordinal), Dynamic GRA (Cardinal).
Normalization is needed because n ot all criteria have the same units of measure. Thus, with normalization, all of them are converted to decimals
Nolberto Munier you did not get my point. Dear Navneet talked about subjective method like AHP (here we collect information from experts). In this case, we can use preference relations (ordinal data). Ordinal data can be collected from human easily and we do not need to normalize ordinal data.
In AHP you collect infotrmation from experts?
I understand that in AHP you use intuition
Regarding normalization, why in AHP you normalize priotities?
Nolberto Munier Criteria and sub-criteria priorities are always normalized in AHP so that they sum to one.
I've studied your book about the limitations of the AHP in recent years. It is truly valuable, and I always suggest it to others. As we know, there are several limitations in the AHP (no benefit to discussing AHP now because it is an old conversation). However, I would like to thank you for your significant contribution to the MCDM context.
Dear Raffaele
Thank you very much for your comments on my book and for the praise on my humble person.
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