Multidimensional scaling is a method of expressing information visually. Rather than show raw numbers, a multidimensional scale chart will show the relationships between variables; things that are similar will appear close together while things that are different will appear far away from one another.
Multidimensional scales show how things stand in relation to one another. For example, if you made a multidimensional scale of city distances in the United States, Chicago would be closer to Detroit than it would be to Phoenix.
An advantage of this method is that you can look at a multidimensional scale and immediately assess how closely related different values are. A disadvantage, though, is that this technique doesn't deal in real numbers — a multidimensional scale of Boston, New York and Los Angeles would look roughly similar to a multidimensional scale of London, Dublin and Buenos Aires, even though the actual figures are profoundly different.
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A multidimensional scale is best used in situations where there's a large amount of data organized in table form. By converting it to a multidimensional scale, you can immediately assess relationships, which is essentially impossible in a table with 10,000 or more different figures — an amount that's entirely feasible.
The disadvantage of this is that a complex formula is necessary to convert raw figures into a multidimensional scale. Therefore, while it's easy to see the relationships between figures, it takes a large amount of effort to create the table. This means that if you're going to use a multidimensional scale, you need to be certain that there's an actual demand for the information it's presenting. Otherwise, you're using your time now for no reason other than to save someone else time in the future.
Multidimensional scaling is generally used in psychology, graphing subject responses to various stimuli. This method is used because researchers can show relationships of importance — i.e., how much importance is placed on different variables. This can be extremely useful, as psychological data tends to be high volume and have many different aspects.
A disadvantage of this is that it adds another layer of subjectivity to psychological data, as modeling tabled data into a multidimensional scale requires some decision-making. Which data will go into the scale? Which multipliers will be used to create relationship figures? This has an effect on the multidimensional scale's accuracy.