Nonparametric ANOVA w/ Between-Subjects Variable: Advice Appreciated

In summary, the individual is looking for a nonparametric alternative to the parametric repeated-measures with a between-subjects variable in order to investigate three sets of subscores of a test taken by three groups of participants. They have considered using Kruskal-Wallis tests and Friedman's ANOVA, and are seeking advice on the best approach for investigating group effects. They also ask if the main criterion for choosing between the two tests is the number of experiment units within each group and if a nonparametric regression model should be used to investigate group effects.
  • #1
Hello Kitty
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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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  • #2


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.
 

What is nonparametric ANOVA with between-subjects variable?

Nonparametric ANOVA with between-subjects variable is a statistical test used to compare the means of three or more independent groups. It is a nonparametric alternative to the parametric ANOVA test, meaning it does not assume a specific distribution for the data.

When should nonparametric ANOVA be used?

Nonparametric ANOVA should be used when the assumptions of the parametric ANOVA test are not met, such as when the data is not normally distributed or when the variances are not equal between groups. It can also be used when the data is on an ordinal or nominal scale.

What is the difference between parametric and nonparametric ANOVA?

The main difference between parametric and nonparametric ANOVA is the assumptions they make about the data. Parametric ANOVA assumes that the data is normally distributed and that the variances are equal between groups, while nonparametric ANOVA makes no assumptions about the distribution of the data.

How is nonparametric ANOVA conducted?

Nonparametric ANOVA is conducted by ranking the data within each group and then comparing the ranks between groups. The test statistic used is typically the Kruskal-Wallis test, which compares the sum of the ranks between groups to the expected value under the null hypothesis.

What are the advantages and limitations of nonparametric ANOVA?

The main advantage of nonparametric ANOVA is that it does not require the data to be normally distributed or for the variances to be equal between groups. It is also more robust to outliers and does not require as large of a sample size as parametric ANOVA. However, nonparametric ANOVA may have less power than parametric ANOVA when the assumptions are met. Additionally, it can only be used for between-subjects designs and may not provide as much information about the differences between groups as parametric ANOVA.

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