MUTLDIMENSIONAL SCALING
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Abstract
This article aims to provide an understanding of multidimensional scaling (MDS), a statistical technique used to analyze high-dimensional data sets by reducing them to lower dimensions. MDS creates a perceptual map to illustrate positions on the axes. A major advantage of MDS is that it does not require predefined evaluation criteria and can analyze data at both group and individual levels. MDS analysis employs the Dispersion Accounted For (DAF) and Tucker's Coefficient of Congruence as indices to measure goodness-of-fit, and the Stress index to assess the quality of the lower-dimensional data obtained. However, MDS has limitations, such as the difficulty in interpreting results, the dependence of outcomes on the distance measurement method used, and potential unsuitability for very high-dimensional data sets. Care must be taken when using MDS, and its limitations should be considered. Although MDS is not yet widely used in education, it is a valuable tool for data analysis. It can be easily implemented using well-known software such as SPSS.
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References
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