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Non-parametric Correlations

In: Statistics and Research Methods in Psychology with Excel

Author

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  • J. P. Verma

    (Lakshmibai National Institute of Physical Education, Department of Sport Psychology)

Abstract

Non-parametric correlations are used to investigate relationship between two variables if any one or both the variables are categorical. In this chapter, several non-parametric correlations such as rank correlation, biserial correlation, tetrachoric correlation, phi coefficient, and contingency coefficient have been discussed and their procedure has been explained by using the solved examples. Rank correlation can also be used for metric data if the normality is severely violated. Biserial correlation is used if one of the variables is measured either on interval or ratio scale and the other is a dichotomous. But if both the variables are dichotomous, then tetrachoric correlation is computed, whereas if both the variables are naturally dichotomous, then phi coefficient is used to measure association. If both the variables are categorical, then contingency coefficient is used for investigating association. The contingency coefficient can be used when one or both the variables are categorized in more than two groups as well.

Suggested Citation

  • J. P. Verma, 2019. "Non-parametric Correlations," Springer Books, in: Statistics and Research Methods in Psychology with Excel, chapter 0, pages 523-565, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-3429-0_13
    DOI: 10.1007/978-981-13-3429-0_13
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