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Normality and ANOVA

Assess normality when comparing three independent groups on a continuous outcome

The assumption of normality is the first statistical assumption that needs to be tested when comparing three or more independent groups on a continuous outcome with ANOVA. Normality of a continuous distribution is assessed using skewness and kurtosis statistics. In order to meet the statistical assumption of normality, skewness and kurtosis statistics should be below an absolute value of 2.0. If either skewness or a kurtosis statistic is above an absolute value of 2.0, then the continuous distribution is assumed to not be normal. ANOVA should not be conducted on continuous variables that violate the assumption of normality. ANOVA should only be conducted on continuous outcomes that are normally distributed.

The steps for assessing normality for ANOVA with skewness and kurtosis statistics in SPSS

1. Click Analyze.

2. Drag the mouse pointer over the Descriptive Statistics drop-down menu.

3. Select Descriptives.

4. Click on the outcome variable to highlight it.

5. Click on the arrow to move the 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.

The steps for interpreting the SPSS output for skewness and kurtosis statistics when using ANOVA

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 you can assume normality of the outcome variable.

Was the assumption of normality met for ANOVA?

Yes
No

Between-Subjects Three Groups
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