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Non-parametric Tests for Psychological Data

In: Statistics and Research Methods in Psychology with Excel

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  • J. P. Verma

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

Abstract

In most of the psychological studies, data that is generated is non-metric; hence, it is essential to know various non-parametric tests that are available for different situations. Non-parametric tests are used for non-metric data, but if assumptions of the parametric tests are violated, these tests can be used for addressing research questions. Several non-parametric tests are available as a substitute for many parametric tests. For example, chi-square test is an option for correlation coefficient; sign test and median/Mann–Whitney U tests are the options for one-sample t-test and two-sample t-test, respectively; Kruskal–Wallis H test is an option for one-way ANOVA; and Friedman’s test is an option for one-way repeated measures ANOVA. The procedure of these tests has been discussed in this chapter by means of examples. After going through this chapter, one should be able to apply chi-square test, runs test, sign test, median test, Mann–Whitney test, Kruskal–Wallis H test, and Friedman’s test.

Suggested Citation

  • J. P. Verma, 2019. "Non-parametric Tests for Psychological Data," Springer Books, in: Statistics and Research Methods in Psychology with Excel, chapter 0, pages 477-521, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-3429-0_12
    DOI: 10.1007/978-981-13-3429-0_12
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