Multidimensional scaling is a method that compares difficult-to-compare items, creating a two-dimensional graph showing similarity between them. It involves multidimensional testing and scaled response, with a five- or seven-point scale. Too many comparisons can lead to inconclusive results. After data collection, a statistical analysis is performed to show numerical similarity between different objects.
Multidimensional scaling is a method used to create comparisons between things that are difficult to compare. The end result of this process is usually a two-dimensional graph showing a level of similarity between various items, all relative to each other. For example, a researcher may give test subjects different varieties of apples and have them compare on different criteria between two apples at a time. Once all the apples are directly compared to each other, the data is plotted on a graph showing how similar one type is to another.
The two components of multidimensional scaling are right in the name, multidimensional testing and scaled response. Both of these concepts are very simple: only the analysis at the end makes this process complex. Multidimensional testing simply means that many factors of the test item are examined simultaneously. In the apple example, things like color, the level of sweetness or acidity, or even how firm the fruit is can be discussed.
Multidimensional scaling scale response refers to the method used to compare factors. This is generally a five- or seven-point scale ranging from unequal to identical. This allows test subjects to interpret questions and give answers based on their feelings rather and concerning themselves with right and wrong. This also has the added benefit of creating a numerical outcome, anywhere from one to five or seven, that researchers can use to mathematically manipulate the data.
These types of studies have both lows and highs for comparison. If there are too many comparisons or things being compared, the data could show artificial similarities where none are present. When there are too many, comparison systems become so overloaded with information that the result is typically inconclusive. Generally, between four and eight comparisons are made between four and 12 items.
In a multidimensional scaling experiment, subjects look at two items at a time. They compare these items on their own, without considering other stages of the test. Eventually, the subjects will compare each object with each other, all in groups of two. For example, the comparison may be between the sweetness of apple one and apple two. The similarity between the sweetness of the two fruits is evaluated on the point scale and the subject moves on to the next question.
After the data has been collected, a program that evaluates the results of the multidimensional scaling experiment performs a complex statistical analysis on the information. First, comparisons on similar factors, such as color, are compared against each other in the absence of all others. Then comparisons of a single item are compared, in the absence of all others, and both are weighted. These results are then aggregated into a final count that shows numerical similarity between multiple different objects.
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