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WIREs Cogn Sci
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Multidimensional scaling

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Abstract The concept of similarity, or a sense of ‘sameness’ among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step‐by‐step fashion how MDS is performed, and key issues that may arise during analysis. WIREs Cogn Sci 2013, 4:93–103. doi: 10.1002/wcs.1203 This article is categorized under: Psychology > Perception and Psychophysics

Stress values (top panels) and explained variance (bottom panels), plotted as a function of dimensions used to locate the points in space, from door‐knockers (left panels) and crimes (right panels), scaled using the ALSCAL algorithm.

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Three‐dimensional outcome of the crime data (scaled using ALSCAL), shown as three separate two‐dimensional plots.

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Inter‐item distance correlations from door‐knocker (left panel) and crime (right panel) stimuli. The correlations show the agreement between the organizations of the spaces derived using ALSCAL and PROXSCAL.

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Two‐dimensional MDS plots for the door‐knocker stimuli. The left panel shows a solution derived using the ALSCAL scaling algorithm, and the right panel shows the same data set analyzed using the PROXSCAL scaling algorithm.

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