A Review of Multidimensional Scaling (MDS) and its Utility in Various Psychological Domains

Abstract

This paper aims to provide a non-technical overview of multidimensional scaling (MDS) so that a broader population of psychologists, in particular, will consider using this statistical procedure. A brief description regarding the type of data used in MDS, its acquisition and analyses via MDS is provided. Also included is a commentary on the unique challenges associated with assessing the output of MDS. Our second aim, by way of discussing representative studies, is to highlight and evaluate the utility of this method in various domains in psychology.The primary utility of statistics is that they aid in reducing data into more manageable pieces of information from which inferences or conclusions can be drawn. Multidimensional scaling (MDS) is an exploratory data analysis technique that attains this aim by condensing large amounts of data into a relatively simple spatial map that relays important relationships in the most economical manner (Mugavin, 2008). MDS can model nonlinear relationships among variables, can handle nominal or ordinal data, and does not require multivariate normality. As such, MDS provides an alternative to methods such as factor analysis and smallest space analysis, for example, in extracting representative information in data exploration (Johnston, 1995;Steyvers et al., 2002).MDS provides a visual representation of dissimilarities (or similarities) among objects, cases or, more broadly, observations. In other words, the technique attempts to find structure in data by rescaling a set of dissimilarities measurements into distances assigned to specific locations in a spatial configuration (Giguère, 2006;Tsogo et al., 2000). As such, points that are closer together on the spatial map

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