Abstract
Simple additive weighting is a well-known method for scoring and ranking alternative options based on multiple attributes. However, the pitfalls associated with this approach are not widely appreciated. For example, the apparently innocuous step of normalizing the various attribute data in order to obtain comparable figures leads to markedly different rankings depending on which normalization is chosen. When the criteria are aggregated using multiplication, such difficulties are avoided because normalization is no longer required. This
removes an important source of subjectivity in the analysis because the analyst no longer has to make a choice of normalization type. Moreover, it also permits the modelling of more realistic preference behaviour, such as diminishing marginal utility, which simple additive weighting does not provide. The multiplicative approach also has advantages when aggregating the ratings of panel members. This method is not new but has been ignored for too long by both practitioners and teachers. We aim to present it in a nontechnical way and illustrate its use with data on business schools.
removes an important source of subjectivity in the analysis because the analyst no longer has to make a choice of normalization type. Moreover, it also permits the modelling of more realistic preference behaviour, such as diminishing marginal utility, which simple additive weighting does not provide. The multiplicative approach also has advantages when aggregating the ratings of panel members. This method is not new but has been ignored for too long by both practitioners and teachers. We aim to present it in a nontechnical way and illustrate its use with data on business schools.
Original language | English |
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Pages (from-to) | 109-119 |
Number of pages | 11 |
Journal | INFORMS Transactions on Education |
Volume | 14 |
Issue number | 3 |
DOIs | |
Publication status | Published - 27 May 2014 |
Keywords
- multiattribute decision making; ranking; scoring; aggregation; selection; teaching management science; teaching decision analysis; multiple criteria