The homogeneity of variance assumption specifies that the variances are equal for the two populations from which the samples are obtained. If this assumption is violated, the t-statistic can cause misleading conclusions for a hypothesis test.
That scores are normally distributed; and 3. That score variance is homogeneous (Vogt & Johnson, 2015). Verified independence is a function of random selection; verified normal distribution is a function of data description and plotting; and verified homogeneity of variance is a function of a test statistic, like an F test.
Each of the 56 measurements was done on an independent sample. 2-way ANOVA analysis indicated that both frequency and time point had a significant effect on the response variable. However, Levene's test indicated the assumption of homoscedasticity was violated. Additionally the data seem non-normal. Here is the output:
However, in a couple of cases, the ANOVA assumptions are slightly violated because the variances aren't equal (according to a Levene's test, using alpha=.05). As I see it, my options are to: 1. Power transform the data and see if it changes the Levene p-val. 2. Use a non-parametric test like a Wilcoxon (if so, which one?). 3.
1. Regardless of which group you choose, the observations within that group have a normal distribution with a common variance, σ 2p That is, a homogeneity of variance assumption is imposed. 2. The difference μ j − μ G has a normal distribution with mean 0 and variance σ 2μ. 3.
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). If the standard deviations are different from each other (exhibit heteroscedasticity), the probability of obtaining a false positive result even though the null hypothesis is true
G08P9. 223 314 359 332 1 136 194 359 338
how to test homogeneity of variance
