Applicability of Dimension Analysis to Data in Psychology

  • Arno Steitz
  • Wolfgang Tschacher
  • Klaus Ackermann
  • Dirk Revenstorf
Part of the Springer Series in Synergetics book series (SSSYN, volume 58)


This paper is motivated by the question of whether dimension analysis is a valid and practical method for the reduction of data in psychology. The paper presents a short introduction to the analysis of chaotic systems by the Grassberger-Procaccia algorithm. General aspects of this method are demonstrated; we tested the limits of dimension analysis depending on signal-to-noise ratio, length of time series, and resolution of measurement. For this purpose, the Hénon map was used as a basic model. The Grassberger-Procaccia algorithm was also applied to a simulated time series of group processes and an empirical time series of smoking behavior. To compensate for artefacts induced by local correlations a revised dimension analysis was performed with the group simulation data. Results suggest that neither group simulation nor cigarette consumption data can be reduced to a low-dimensional deterministic system.


Time Series Chaotic System Smoking Behavior Dimension Analysis Correlation Dimension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Arno Steitz
  • Wolfgang Tschacher
  • Klaus Ackermann
  • Dirk Revenstorf

There are no affiliations available

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