When interpreting the statistical significance of multivariate kurtosis and skewness, it is important to consider their implications for the distribution of the data being analyzed. While I can provide a general understanding, I cannot provide specific references as I do not have direct access to external sources. However, I can provide you with some guidance on how to interpret these results.

Multivariate kurtosis measures the degree of peakedness or flatness of a multivariate distribution, while multivariate skewness measures the asymmetry of the distribution. The p-values associated with these statistics indicate their statistical significance.

In your scenario, where the multivariate kurtosis has a p-value greater than 0.5 (not significant), while the multivariate skewness has a p-value less than 0.5 (significant), it suggests the following:

1. Multivariate Kurtosis: The lack of statistical significance in the multivariate kurtosis suggests that the degree of peakedness or flatness of the distribution is not significantly different from that of a normal distribution. However, it does not necessarily imply that the distribution is normally distributed.

2. Multivariate Skewness: The statistical significance of the multivariate skewness indicates that the distribution significantly deviates from symmetry. A p-value less than 0.5 suggests evidence of significant skewness in the data.

Based on these results, you can conclude that the data you are analyzing exhibits significant skewness, indicating a departure from symmetry. However, the lack of significance in multivariate kurtosis does not provide strong evidence regarding the shape of the distribution. It is essential to consider additional factors and conduct further analyses to gain a comprehensive understanding of the distribution and its characteristics.

It's worth noting that the interpretation of multivariate kurtosis and skewness can vary depending on the specific statistical method or software used for their calculation. Additionally, the interpretation should be made in the context of the research question and the underlying assumptions of the statistical tests employed.