What is the non-parametric alternative to a one-way ANOVA?
The Kruskal–Wallis test by ranks, Kruskal–Wallis H test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric method for testing whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes.
How to check if data are heteroscedastic in an ANOVA?
To learn how to check this and what to do if the data are heteroscedastic (have different standard deviations in different groups). One of the assumptions of an anova and other parametric tests is that the within-group standard deviations of the groups are all the same (exhibit homoscedasticity).
When to use a non parametric test instead of an ANOVA?
The most commonly used alternative to one-way anova is Welch’s anova, sometimes called Welch’s t –test when there are two groups. Non-parametric tests, such as the Kruskal–Wallis test instead of a one-way anova, do not assume normality, but they do assume that the shapes of the distributions in different groups are the same.
Can you do one way ANOVA with raw data?
One-way ANOVA would definitely be the way to go, however upon conducting normality tests on my data, heteroskedascity seems to the main issue. My raw data, without any transformation, produced a ratio of variances ( F max = 19.1) which is very much higher than the critical value ( F c r i t = 4.16) and therefore I cannot perform one-way ANOVA.
Are there any options for dealing with heteroscedastic data?
There are a number of options available when dealing with heteroscedastic data. Unfortunately, none of them is guaranteed to always work. Here are some options I’m familiar with: Update: Here is a demonstration in R of some ways of fitting a linear model (i.e., an ANOVA or a regression) when you have heteroscedasticity / heterogeneity of variance.