Hello Oshandhi,

Ten DVs...that's a busy (and not-so-little) data set, isn't it?

Here are a couple of suggestions for how to disentangle, present, and explain interactions:

1. Article Improving Present Practices in the Visual Display of Interactions

2. Article Reporting Interaction Effects: Visualization, Effect Size, a...

3. https://www.sciencedirect.com/science/article/abs/pii/S0895435612000558

Good luck with your work.

A slightly different aspect regarding your question "how the three-way interactions of A, B, and C impact changes in DVs".

The "how" is in the estimates, not in the tests/p-values. The tests answer the question if your sample data are sufficient to generalize the signs of these estimates to the sampled population. That's a different question. Large p-values indicate insufficient information (from the sample), not the absence of an effect (in the population).

As David Morse hinted, with 10 DVs, you have a very big multiplicity problem. How do you intend to address that? Even though they are nearly 20 years old now, I think the two Lancet articles by Schulz & Grimes (2005) are great resources on multiplicity.

Article Schulz K, Grimes DMultiplicity in randomised trials I: endpo...

Article Multiplicity in randomised trials II: Subgroup and interim analyses

See also Craig Bennett's interesting poster describing results from an fMRI study carried out on a dead salmon:

• http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf

HTH.

I am planning to conduct MANOVA/PERMANOVA on the dataset, followed by linear mixed-effects models for each dependent variable. Additionally, I will perform correlation and PCA to visualise the multivariate relationships.