The article discusses how researchers analyze data with multiple variables using a method called multivariate analysis of variance (MANOVA). They compare different statistical tests like Wilks' Λ, Roy's θ, Pillai's V(s), and the Lawley–Hotelling statistic U(s) to see which one is most effective. The researchers also look at how to check if the assumptions of MANOVA are met and how to compare growth curves in response to treatments over time. Overall, the study provides practical methods for analyzing complex data sets with multiple variables and offers insights into how different statistical tests can be used to draw meaningful conclusions.