Full course description
Date & Time: Self-Paced
Location: Online Only
Presented By: Higher Ed Plus
Description:
This course explores non-parametric statistical tests as robust alternatives to parametric methods, when assumptions of normality or homogeneity of variance are violated. Participants will learn how to conduct chi-squared tests of frequencies and independence in SPSS, perform associated power analyses using G*Power, and interpret effect sizes using Cohen’s w. The course also covers the Wilcoxon-Mann-Whitney U-test and Kruskal-Wallis H-test, including appropriate use cases, critical value tables for small samples, and unique power characteristics. Learners will calculate and interpret effect sizes such as θ and eta-squared, and examine the challenges associated with conducting multiple significance tests and p-hacking. The course concludes with a comparison of frequentist and Bayesian statistical frameworks.
Disclaimer:
Before proceeding with this Canvas module, please note:
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