Contents
Are there unequal sample sizes for mixed ANOVA?
However, all these different groups have different numbers of examinees. The first group has 490 participants, the second group has 1919 participants and the third group has 529 participants. Thus, I can say that I have unequal sample sizes for Mixed ANOVA.
How are independent samples used in ANOVA?
Independent samples t-tests are essentially a simplificiation of a one-way ANOVA for only two groups. In fact, if you run your t-test as an ANOVA, you’ll get the same p-value. And the between-groups F statistic will be the square of the t statistic you got in your t-test.
How to deal with unequal number of samples?
Hello Eshvendar, nowadays, most of the software statistical packages deal with the issue of unequal sample sizes. That only thing you have to do is to take the different sample size into consideration in your computation.
Is it OK to have different sample sizes?
It is ok if your groups have different sample sizes as long as each has a minimum number of cases in them (min 30 cases each). If your Leven’s test is significant then inequality of variances are assumed and you should use the P value and assess its significance under unequal variances.
Can you do ANOVA to tell which group is statistically significant?
Yes, you can. But bear in mine that, if the ANOVA result suggest that the difference within groups are statistically significant. ANOVA doesn’t tell you which specific group is statistically different. Thus, you might want to either conduct some post hoc test (LSD, HSD, etc) to determine which mean difference is/are statistically significant.
When are unequal sample sizes are and are not a problem?
In your statistics class, your professor made a big deal about unequal sample sizes in one-way Analysis of Variance (ANOVA) for two reasons. 1. Because she was making you calculate everything by hand. Sums of squares require a different formula* if sample sizes are unequal, but statistical software will automatically use the right formula.
What is the statistical advantage of equal sample sizes?
Additionally, while equal-sized groups maximise statistical power, the advantage is easily overstated. An experiment with 30+30 participants has a 76% chance to detect a systematic difference of 0.7 standard deviations between the two group means; for an experiment with 20+40 participants, this probability is 71%.
Why do we need equal sample sizes in randomised trials?
We cringe at the pervasive notion that a randomised trial needs to yield equal sample sizes in the comparison groups. Unfortunately, that conceptual misunderstanding can lead to bias by investigators who force equality, especially if by non-scientific means.