The SmartPLS ++data view++ provides information about the excess kurtosis and skewness of every variable in the dataset. You can interpret the values as follows:

"Skewness assesses the extent to which a variable’s distribution is symmetrical. If the distribution of responses for a variable stretches toward the right or left tail of the distribution, then the distribution is characterized as skewed. A negative skewness indicates a greater number of larger values, whereas a positive skewness indicates a greater number of smaller values. As a general guideline, a skewness value between −1 and +1 is considered excellent, but a value between −2 and +2 is generally considered acceptable. Values beyond −2 and +2 are considered indicative of substantial nonnormality." (Hair et al., 2022, p. 66).

Kurtosis is a measure of whether the distribution is too peaked (a very narrow distribution with most of the responses in the center). A positive value for the kurtosis indicates a distribution more peaked than normal. In contrast, a negative kurtosis indicates a shape flatter than normal. Analogous to the skewness, the general guideline is that if the kurtosis is greater than +2, the distribution is too peaked. Likewise, a kurtosis of less than −2 indicates a distribution that is too flat. When both skewness and kurtosis are close to zero, the pattern of responses is considered a normal distribution (George & Mallery, 2019)." (Hair et al., 2022, p. 66).

When both skewness and kurtosis are zero (a situation that researchers are very unlikely to ever encounter), the pattern of responses is considered a normal distribution.

References

• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3 ed.). Thousand Oaks, CA: Sage.