By Joel Cadwell
My screen has been filled with ads from BayesiaLab since I downloaded their free book. Just as I began to have regrets, I received an email invitation to try out their demo datasets. I was especially interested in their perfume ratings data. In this monadic product test, each of 1,321 French women was presented with only one of 11 perfumes and asked to evaluate on a 10-point scale a series of fragrance-related adjectives along with a few user-imagery descriptors. I have added the 6-point purchase intent item to the analysis in order to assess its position in this network.
Can we start by looking at the partial correlation network? I will refer you to my post on Driver Analysis vs. Partial Correlation Analysis and will not repeat that more detailed overview.
Each of the nodes is a variable (e.g., purchase intent is located on the far right). An edge drawn between any two nodes shows the partial correlation between those two nodes after controlling for all the other variables in the network. The color indicates the sign of the partial correlation with green for positive and red for negative. The size of the partial correlation is indicated by the thickest of the edge.
Simply scanning the map reveals the underlying structure of global connections among even more strongly joined regions:
- Northwest – In Love / Romantic / Passionate / Radiant,
- Southwest – Bold / Active / Character / Fulfilled / Trust / Free,
- Mid-South – Classical / Tenacious / Quality / Timeless / High End,
- Mid-North – Wooded / Spiced,
- Center – Chic / Elegant / Rich / Modern,
- Northeast – Sweet / Fruity / Flowery / Fresh, and
- Southeast – Easy to Wear / Please Others / Pleasure.
Unlike the Probabilistic Structural Equation Model (PSEM) in Chapter 8 of BayesiaLab’s book, my network is undirected …read more
Source:: r-bloggers.com