Adjusted R-square

Analyse qualitative data. How to

Analyse quantitative data. How to -

ANCOVA (Analyses of covariance)

ANOVA (Analyses of variance)

Applied research

Average

Bias

[...]

In this article

Table of contents

[[showindex]]
A better way to check the assumptions of normality is to calculate the skewness and the kurtosis. Both values should lie between the limits of -0.05 and +0.05 or, when you are less strict, between -1.00 and +1.00.

Furthermore, two tests on normality can be applied: the Kolmogorov-Smirnov test and the Shapiro-Wilks test. Both test should be not-sigificant to conclude that the variable is normally distributed.

The most common reason for a variable not to be normally distributed is because there are some outliers. It is hard to give reasons why an outlier should be excluded. Do we have to change the data from the real world, because than it fits with the assumptions of statistics?

- Variable
- Nominal level
- Ordinal level
- Interval level
- Ratio level

- Kolmogorov-Smirnov test
- Shapiro-Wilks test
- Skewness
- Kurtosis
- Outlier

Place comment