How to Interpret Excess Kurtosis and Skewness

In the data view of SmartPLS, you can find information about the excess kurtosis and skewness of every variable in the dataset. Here's how to interpret these values:

Skewness: Skewness measures the symmetry of a variable's distribution. If the distribution stretches toward the right or left tail, it's skewed. Negative skewness indicates more larger values, while positive skewness indicates more smaller values. A skewness value between -1 and +1 is excellent, while -2 to +2 is generally acceptable. Values beyond -2 and +2 suggest substantial nonnormality (Hair et al., 2022, p. 66).

Kurtosis: Kurtosis indicates whether the distribution is too peaked or flat compared to a normal distribution. Positive kurtosis means a more peaked distribution, while negative kurtosis means a flatter one. A kurtosis greater than +2 suggests a too peaked distribution, while less than -2 indicates a too flat one. When skewness and kurtosis are close to zero, it's considered a normal distribution (Hair et al., 2022, p. 66).

In the rare scenario where both skewness and kurtosis are zero, the pattern of responses is considered a normal distribution.