Contents
How does unequal sample size affect ANOVA?
Problems with Unequal Sample Sizes Unequal sample sizes can lead to: Unequal variances between samples, which affects the assumption of equal variances in tests like ANOVA. Having both unequal sample sizes and variances dramatically affects statistical power and Type I error rates (Rusticus & Lovato, 2014).
What is the sample size for ANOVA?
Using the criteria above, the sample size needed for the one-way ANOVA, testing for differences on one independent variable with two groups, is 128, the same as the independent samples t-test.
What is minimum sample size for ANOVA?
How to conduct two-way ANOVA with unequal sample sizes?
To see a complete example of how two-way ANOVA with unequal sample sizes can be conducted in R, please download the two-way ANOVA with unequal sample sizes example (.txt) file. To leave a comment for the author, please follow the link and comment on their blog: R Tutorial Series.
When to conduct a two way ANOVA in R?
When the sample sizes within the levels of our independent variables are not equal, we have to handle our ANOVA differently than in the typical two-way case. This tutorial will demonstrate how to conduct a two-way ANOVA in R when the sample sizes within each level of the independent variables are not the same.
When to use weighted or unweighted means in an ANOVA?
Generally, this comes down to examining the correlation between the factors and the causes of the unequal sample sizes en route to choosing whether to use weighted or unweighted means – a decision which can drastically impact the results of an ANOVA. This tutorial will demonstrate how to conduct ANOVA using both weighted and unweighted means.
Can you check normality of residuals in ANOVA?
Yes, you can 🙂 ANOVA doesn’t assume equal sample sizes. It just assumes equal variances and normal distribiution in each group. So you need to check normality (e.g. Shapiro-Wilk test) in each grup separately, or (easier) check normality of residuals.