Contents
Does MANOVA require normality?
In order to use MANOVA the following assumptions must be met: Observations are randomly and independently sampled from the population. Each dependent variable has an interval measurement. Dependent variables are multivariate normally distributed within each group of the independent variables (which are categorical)
Can you do a regression on non-normal data?
In linear regression, errors are assumed to follow a normal distribution with a mean of zero. It seems like it’s working totally fine even with non-normal errors. In fact, linear regression analysis works well, even with non-normal errors. But, the problem is with p-values for hypothesis testing.
What to do when data is non-normal?
Many practitioners suggest that if your data are not normal, you should do a nonparametric version of the test, which does not assume normality. From my experience, I would say that if you have non-normal data, you may look at the nonparametric version of the test you are interested in running.
Does data have to be normally distributed for logistic regression?
First, logistic regression does not require a linear relationship between the dependent and independent variables. Second, the error terms (residuals) do not need to be normally distributed. Third, logistic regression requires there to be little or no multicollinearity among the independent variables.
Are there any nonparametric MANOVA approaches for missing data?
This dissertation addresses the issues of violation of multivariate normality assumption and missing data, focusing on the non-parametric multivariate Kruskal-Wallis (MKW) test, likelihood-based and permutation-based methods. First, an R-based program is written to compute the p-value of MKW test for group comparison.
Are there any univariate outliers in the MANOVA?
There are none. There are, however, several univariate outliers. I tried to transform the data but when I did it, I transformed both the levels of the IV so the Kolmorgorov-Smirnov test remained significant. My question is whether I now need to take the Perseverance and Social anxiety out of the MANOVA and run a non-parametric test on it.
Which is better permutation based MANOVA or classical MANOVA?
Simulation studies show that the permutation-based MKW test provides better coverage and higher power level than likelihood-based MKW test and classical MANOVA. Second, an extension of MKW test is proposed for multivariate data with missingness. The proposed method retrieves information in partially observed cases and is permutation-based.
When to use Kruskal Wallis test for one way ANOVA?
One way ANOVA when the data are not normally distributed (The Kruskal-Wallis test). Suppose you have a one way design, and want to do an ANOVA, but discover that your data are seriously not normal? Just like with the MWU test as “replacement” for the t-test, there is the Kruskal-Wallis test for a one way ANOVA.