MDS Analysis Using Basic MDS Model

  • Cody S. Ding
Chapter

Abstract

Use of basic MDS model is explained. Examples are provided. How the basic model is estimated is discussed, although readers can skip this part. Interpretation of results from basic MDS analysis is further explained, including decision on number of dimensionality.

Keyword

Data structure Estimation Dimensionality Interpretation 

References

  1. Bond, F., Hayes, S. C., Baer, R. A., Carpenter, K. M., Guenole, N., Orcutt, H. K., et al. (2011). Preliminary psychometric properties of the acceptance and action questionnaire–II: A revised measure of psychological inflexibility and experiential avoidance. Behavior Therapy, 42, 676–688.CrossRefGoogle Scholar
  2. Bond, F. W., & Bunce, D. (2003). The role of acceptance and job control in mental health, job satisfaction, and work performance. Journal of Applied Psychology, 88, 1057–1067.CrossRefGoogle Scholar
  3. Borkovec, T. D., Hazlett-Stevens, H., & Diaz, M. L. (1999). The role of positive beliefs about worry in generalized anxiety disorder and its treatment. Clinical Psychology and Psychotherapy, 6, 126–138.CrossRefGoogle Scholar
  4. Bühler, J., Keller, F., & Läge, D. (2014). Activation as an overlooked factor in the BDI–II: A factor model based on Core symptoms and qualitative aspects of depression. Psychological Assessment, Advance online publication.  https://doi.org/10.1037/a0036755.
  5. Chawla, N., & Ostafin, B. (2007). Experiential avoidance as a functional dimensional approach to psychopathology: An empirical review. Journal of Clinical Psychology, 63, 871–890.  https://doi.org/10.1002/jclp.20400.CrossRefGoogle Scholar
  6. Cohen, A. (2008). The underlying structure of the Beck Depression Inventory II: A multidimensional scaling approach. Journal of Research in Personality, 42, 779–786.  https://doi.org/10.1016/j.jrp.2007.09.007.CrossRefGoogle Scholar
  7. Data Theory Scaling System Group. (n.d.). PROXSCAL (Version 1.0). Leiden university. Netherlands: Faculty of Social and Behavioral Sciences.Google Scholar
  8. Davison, M. L. (1983). Multidimensional scaling. New York: Wiley.MATHGoogle Scholar
  9. Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.MATHGoogle Scholar
  10. Guttman, L. (1968). A general non-metric technique for finding the smallest co-ordinate space for a configuration of points. Psychometrika, 33, 469–506.CrossRefGoogle Scholar
  11. Hayes, S. C., Strosahl, K. D., Wilson, K. G., Bissett, R. T., Pistorello, J., Toarmino, D., et al. (2004). Measuring experiential avoidance: A preliminary test of a working model. Psychological Record, 54, 553–5878.CrossRefGoogle Scholar
  12. Kline, R. B. (2010). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford.MATHGoogle Scholar
  13. Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.MathSciNetCrossRefGoogle Scholar
  14. Sibson, R. (1972). Order invariant methods for data analysis. Journal of Royal Statisical Society (B), 364, 311–349.MathSciNetMATHGoogle Scholar
  15. Wells, A., & Papageorgiou, C. (1995). Worry and the incubation of intrusive images following stress. Behaviour Research and Therapy, 33, 579–583.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Cody S. Ding
    • 1
    • 2
  1. 1.Department of Education Science and Professional ProgramUniversity of Missouri-St. LouisSt. LouisUSA
  2. 2.Center for NeurodynamicsUniversity of Missouri-St. LouisSt. LouisUSA

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