title: Goodness-of-Fit Assessment in Multidimensional Scaling and Unfolding.
abstract: Judging goodness of fit in multidimensional scaling requires a comprehensive set of diagnostic tools instead of relying on stress rules of thumb. This article elaborates on corresponding strategies and gives practical guidelines for researchers to obtain a clear picture of the goodness of fit of a solution. Special emphasis will be placed on the use of permutation tests. The second part of the article focuses on goodness-of-fit assessment of an important variant of multidimensional scaling called unfolding, which can be applied to a broad range of psychological data settings. Two real-life data sets are presented in order to walk the reader through the entire set of diagnostic measures, tests, and plots. R code is provided as supplementary information that makes the whole goodness-of-fit assessment workflow, as presented in this article, fully reproducible.
authors: a Harvard University.
authors: Patrick
authors: P
authors: Mair
authors: b University of Münster.
authors: Ingwer
authors: I
authors: Borg
authors: c Vienna University of Economics and Business.
authors: Thomas
authors: T
authors: Rusch
mesh_terms: D000465:Algorithms; D001290:Attitude; D002681:China; D003627:Data Interpretation, Statistical; D055866:Earthquakes; D006040:Goals; D006801:Humans; D008960:Models, Psychological; D015999:Multivariate Analysis; D010551:Personality; D011569:Psychiatric Status Rating Scales; D012984:Software; D013313:Stress Disorders, Post-Traumatic / Q000175:diagnosis / Q000209:etiology / Q000523:psychology
keywords: Multidimensional scaling; goodness-of-fit; permutation tests; unfolding