Workshop (1 or 2 days; level open, depending on participants)
Applied Multidimensional Scaling and Unfolding, with applications in research on personal values, work values, analysis of employee surveys, similarity judgments, fear of crime, delinquency, acceptance of legal norms.
Multidimensional scaling (MDS) is a very useful and broadly applicable statistical method for the analysis of proximity data (e.g., inter-correlations, direct similarity ratings, preference data, co-occurrence data). MDS represents these data, as precisely as possible, as distances among points of a multi-dimensional (usually: 2-dimensional) geometric space (a ”plane”). This representation can be interpreted in terms of distances, neighborhoods, regions, dimensions, etc. In contrast to techniques such as cluster analysis or factor analysis, MDS leads to a continuous mapping which is often ideal for exploring the structure of the data but also for testing various forms of theoretical hypotheses about the inter-relations of the variables, and also for guiding discussions that aim at deriving data-based actions in applied settings such as employee surveys. In science, one finds thousands of applications of MDS. Some content areas (e.g., research on personal values) are even closely linked to MDS.
Unfolding is a related method for preference data. Unfolding has been used rarely in research (mainly for technical reasons) but recently has been re-discovered as a method that allows not only to scale the variables but also the persons at the same time.
Both MDS and Unfolding have seen many new developments in recent years such as statistical tests of model fit, model stability, robustness, and replicability; methods for interpreting MDS solutions; or combining MDS with factor analysis, regression, or discrimination analysis. Much of this has been built into the R package SMACOF, and so we will use mostly SMACOF in this workshop.
The workshop is an introduction to MDS and Unfolding for the applied user. Its level can be gauged such that it matches the methodological know-how of the participants. Prior knowledge of MDS is not required. You can bring your own data and questions. You need to bring your own computer, preferably with R already installed.
Borg, I. & Groenen, P.J.F. (2005). Modern multidimensional scaling: Theory and applications (2nd edition). New York: Springer.
Borg, I., Groenen, P.J.F. & Mair, P. (2018). Applied multidimensional scaling and unfolding (2nd edition). New York: Springer.
Borg, I., Mair, P. & Cetron, J. (2020). Facet theory and multidimensional scaling. In Shye, S., Solomon, E. & Borg, I. (Eds.), Conference Proceedings: 17th International Facet Theory Conference 2021 (pp. 18-29). Prague, CR. https://research.library.fordham.edu/ftc/ftc_proceedings/2020/1/
Borg, I., Dobewall, H., & Aavik, T. (2016). Personal values and their structure under universal and lexical approaches. Personality and Individual Differences, 96, 70-77.
Borg, I. & Bardi, A. (2016). Should ratings of the importance of personal values be centered? Journal of Research in Personality, 63, 95-101.
Mair, P., Borg, I., & Rusch, T. (2016). Goodness-of-fit evaluation in multidimensional scaling and unfolding. Multivariate Behavioral Research, 51, 772-789.
Borg, I., Bardi, A., & Schwartz, S. (2017). Does the value circle exist within persons or only across persons? Journal of Personality, 85, 151–162.
Borg, I., Herman, D., & Bilsky, W. (2017). A closer look at personal values and delinquency. Personality and Individual Differences, 116, 171–178.
Borg, I., Hertel, G., & Hermann, D. (2017). Age and personal values: Similar value circles with shifting priorities. Psychology and Ageing, 32, 636-641.
Borg, I. (2018). A note on the positive manifold hypothesis. Personality and Individual Differences, 134, 13-15.
Borg, I., Hermann, D., Bilsky, W., & Pöge, A. (2019). Do the PVQ and the IRVS scales for personal values support Schwartz’s value circle model or Klages’ value dimensions model? Measurement Instruments for the Social Sciences, 2:3, 1-14.
Borg, I. (2019). Age- and gender-related diﬀerences in the structure and the meaning of personal values. Personality and Individual Differences, 138, 336-343.
Borg, I., Hertel, G., Krumm, S., & Bilsky, W. (2019). Work values and facet theory: From inter-correlations to individuals. International Studies of Management and Organizations, 49(3), 283-302.
Bilsky, W., Borg, I., & Hermann, D. (2020). Utilizing personal values to explain persons’ attitudes towards legal norms. European Journal of Criminology, 17, 1-21.
Borg, I. & Hermann, D. (2020). Personal values of lawbreakers. Personality and Individual Differences, 164: 110104.
Borg, I. (2020). Unfolding persons’ personal values in ideal-point and vector models. Personality and Individual Differences, 166: 110206.
Borg, I., Bilsky, W., & Hermann, D. (2020). Kriminalitätsfurcht und die Einstellung zur Sicherheitslage in der Stadt. Kriminologie - Das Online-Journal | Criminology – The Online Journal, 2(4), 467-490.
Borg, I. (2021). Age and the subjective importance of personal values. Personality and Individual Differences, 173, 110605.
Borg, I. & Hermann, D. (2021). Die Verortung von Corona-bezogenen Einstellungen, Compliance und Ängsten im Werteraum. Kriminologie - Das Online-Journal | Criminology - The Online Journal, 5(4), 383-403.
Borg, I. & Hermann, D. (2021). Inside and outside perspectives on the relation of people’s personal values and their acceptance of legal norms. Macro Management & Public Policies, 3(4), 1-13. https://ojs.bilpublishing.com/index.php/mmpp/article/view/4178
Borg, I., Hermann, D. & Bilsky, W. (2022). The perceived seriousness of crimes: inter-individual commonalities and differences. Quality and Quantity. doi: 10.1007/s11135-022-01379-9