…we had a fantastic group of researchers. Participants dived in with curiosity, asking sharp questions, testing ideas, and applying new techniques to their own data.

And the coffee breaks came with this view. ☕🏞️

"So EJ, say I am tossing a coin..."

I could collect more data, and the Bayesian approach allows me to monitor the evidence (e.g., the BF) as the data come in. I could also include theory or results from earlier research and update my knowledge. This is the kind of cumulative science I like to see! :-)

The phrase about results "not providing a solid basis for cumulative science" is about edges with anecdotal evidence. If I do not have enough evidence to draw a conclusion about an edge, then any decision is "risky". I would be happy to learn this if my theory or intervention builds on the edge.

Interestingly, we also found evidence for the absence of many edges, a result that doesn't match the predictions of the unidimensional factor model, suggesting it wouldn't fit the data well. Thus, we likely need network or equivalently higher-order factor models to describe these data!

Hi Miri, I don’t think it’s about partial correlations but about model complexity, as Karoline said. Unidimensional factor models are also based on partial correlations and are often robust, but they also have far less parameters than network models.

Je was me net voor!

This is a collaborative effort with amazing colleagues: Lourens Waldorp, Nikola Sekulovski, and @jmbh.bsky.social. 🙌 Thanks to this team for their hard work in advancing Bayesian methods in network analysis!