Nonparametric ANOVA w/ Between-Subjects Variable: Advice Appreciated

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SUMMARY

This discussion centers on the use of nonparametric alternatives to parametric repeated-measures ANOVA when analyzing subscores from three groups of participants. The user initially employed the parametric repeated-measures ANOVA but shifted to the Kruskal-Wallis test in SPSS due to assumption violations. Key considerations include whether to rank scores for each participant and use Friedman's ANOVA, particularly when the number of experimental units is unequal. The choice between the Kruskal-Wallis test and Friedman's ANOVA hinges on the equality of group sizes, with implications for subsequent nonparametric regression analysis to investigate group effects.

PREREQUISITES
  • Understanding of nonparametric statistical tests, specifically Kruskal-Wallis and Friedman's ANOVA.
  • Familiarity with SPSS for statistical analysis.
  • Knowledge of experimental design principles, particularly with between-subjects variables.
  • Basic concepts of ranking data for nonparametric tests.
NEXT STEPS
  • Research the implementation of Kruskal-Wallis tests in SPSS for independent samples.
  • Learn about Friedman's ANOVA and its application in nonparametric repeated-measures designs.
  • Investigate nonparametric regression models for analyzing group effects post-ANOVA.
  • Explore the assumptions and limitations of nonparametric tests in experimental design.
USEFUL FOR

This discussion is beneficial for statisticians, researchers in experimental design, and anyone involved in analyzing nonparametric data, particularly in educational or psychological testing contexts.

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I'm currently looking to use a nonparametric alternative to the parametric repeated-measures with a between-subjects variable. I'm aiming to investigate three sets of subscores of a test taken by three groups of participants. I was initially using the parametric repeated-measures ANOVA using the within-subject variable tested at 3 levels, together with the between-subject variable Group tested at 3 levels. However, since it violated some of the assumptions to be met, I started considering using a non-parametric alternative.

To this end, I ranked each set of the subscores separately, and performed Kruskal-Wallis tests using the Tests for Several Independnt Samples in SPSS. Is it the right way of going about this? Or should I rank scores for each participant and perform Friedman's ANOVA? If I go with the latter, since this is a non-parametric equivalent of a one-way repeated-measures ANOVA, how can I investigate group effects? Should I carry out multiple independent-samples tests afterwards?

I hope my questions make sense and are not too silly.
Any suggestion or advice would be deeply appreciated.

Thanks.
 
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I am interested in experiment design and statistics although I am pretty new. Hope to learn something from some discussion and forgive me if I sound too dumb.

Am I right to say that the main criterion for choosing between the Kruskal Wallis test or the Friedman test is whether the number of experiment units within each group/treatment are the same. So you should use the Kruskal Wallis test if you have unequal number of experiment units within each group, otherwise you may choose to use the Friedman test.

I guess the way you have to go about with investigating group effects depend on the differences between the groups? Are the groups different in a measurable (speed, IQ, weight) or non-measurable sort of way(color, gender, race)?

If your nonparametric anova suggests that group/treatment has an effect on the results, I suppose you have to use a nonparametric regression model also, to investigate group effects.
 

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