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Methods of Data Analysis

In: Personality Traits and Drug Consumption

Author

Listed:
  • Elaine Fehrman

    (Rampton Hospital, Men’s Personality Disorder and National Women’s Directorate)

  • Vincent Egan

    (University of Nottingham, Department of Psychiatry and Applied Psychology)

  • Alexander N. Gorban

    (University of Leicester, Department of Mathematics)

  • Jeremy Levesley

    (University of Leicester, Department of Mathematics)

  • Evgeny M. Mirkes

    (University of Leicester, Department of Mathematics)

  • Awaz K. Muhammad

    (University of Leicester, Department of Mathematics
    College of Education, Salahaddin University-Erbil, Department of Mathematics)

Abstract

In this chapter, we give a brief outline of the methods of data analysis used, from elementary T-scores to nonlinear principal component analysis (PCA), including data normalisation, quantification of categorical attributes, categorical principal component analysis (CatPCA), sparse PCA, the method of principal variables, the original ‘double’ Kaiser selection rule, k-nearest neighbours for various distances, decision tree with various split criteria (information gain, Gini gain or DKM gain), linear discriminant analysis, Gaussian mixture, probability density function estimation by radial basis functions, logistic regression, naïve Bayes approach, random forest, and data visualisation on the nonlinear PCA canvas.

Suggested Citation

  • Elaine Fehrman & Vincent Egan & Alexander N. Gorban & Jeremy Levesley & Evgeny M. Mirkes & Awaz K. Muhammad, 2019. "Methods of Data Analysis," Springer Books, in: Personality Traits and Drug Consumption, chapter 0, pages 35-59, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-10442-9_3
    DOI: 10.1007/978-3-030-10442-9_3
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