Non-Traditional Correlation Analysis: Explanatory Power and Opportunities for Knowledge Discovery in Democracy Studies


  •  Nikolay Tretyakov    
  •  Pavel Golosov    
  •  Saif Mouhammad    

Abstract

The possibility of successful applications of the modified correlation coefficient is demonstrated. The latter was proposed by Lukashin nearly twenty five years ago and has been unused since then. A multivariate generalization of this coefficient is proposed. The modified correlation coefficients provide an efficient tool to develop a new multivariate classification method, i.e. a technique for grouping of objects that occurs together with their ranking. As an example of application of the new method, the data of Freedom House is used. NCA (Non-traditional Correlation Analysis), along with similar unconventional methods as FCA (Formal Concept Analysis) and QCA (Qualitative Comparative Analysis) allow to gain additional knowledge from existing databases and numerous ratings which are produced by different agencies. The latters often lack time and opportunities to deeply analyze them, even to go beyond a simple “averaging”. NCA may give additional opportunities for social researchers to understand social phenomena in its complexity, for in-depth analysis and interpretation of structure of data, to build “hierarchical typologies”, and broadly, for data mining and additional knowledge discovery.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1913-1844
  • Issn(Onlne): 1913-1852
  • Started: 2007
  • Frequency: monthly

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Google-based Impact Factor (2018): 6.49

h-index (January 2018): 30

i10-index (January 2018): 163

h5-index (January 2018): 19

h5-median(January 2018): 25

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