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.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:5354 |
| Date | 11 1900 |
| Creators | Mair, Patrick, Borg, Ingwer, Rusch, Thomas |
| Publisher | Taylor & Francis Group |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://www.tandfonline.com/doi/full/10.1080/00273171.2016.1235966, http://www.routledge.com/, http://www.tandfonline.com/toc/hmbr20/current, http://epub.wu.ac.at/5354/ |
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