Skewness and kurtosis statistics are used to assess the normality of a continuous variable's distribution. The statistical assumption of normality must always be assessed when conducting inferential statistics with continuous outcomes.
Any skewness or kurtosis statistic above an absolute value of 2.0 is considered to mean that the distribution is non-normal. Skewness and kurtosis statistics below an absolute value of 2.0 denote a normal distribution.
Any skewness or kurtosis statistic above an absolute value of 2.0 is considered to mean that the distribution is non-normal. Skewness and kurtosis statistics below an absolute value of 2.0 denote a normal distribution.
1. Click Analyze.
2. Drag the mouse pointer over the Descriptive Statistics drop-down menu.
3. Select Descriptives.
4. Click on the continuous outcome variable to highlight it.
5. Click on the arrow to move the outcome variable into the Variable(s): box.
6. Click the Options tab.
7. Deselect Minimum and Maximum boxes under the Dispersion section.
8. Select the Kurtosis and Skewness boxes under the Distribution section.
9. Click Continue.
10. Select the Save standardized values as variables box.
11. Click OK.
2. Drag the mouse pointer over the Descriptive Statistics drop-down menu.
3. Select Descriptives.
4. Click on the continuous outcome variable to highlight it.
5. Click on the arrow to move the outcome variable into the Variable(s): box.
6. Click the Options tab.
7. Deselect Minimum and Maximum boxes under the Dispersion section.
8. Select the Kurtosis and Skewness boxes under the Distribution section.
9. Click Continue.
10. Select the Save standardized values as variables box.
11. Click OK.
1. Under the skewness and kurtosis columns of the Descriptive Statistics table, if the Statistic is less than an absolute value of 2.0, then one can assume normality of the variable.