The quotes from Nature EiC Magdalena Skipper about whether journals should be checking for errors/data quality as part of peer review are quite surprising to me. https://www.wsj.com/science/whats-wrong-with-peer-review-e5d2d428?st=dhrnljoa74fujcv&reflink=desktopwebshare_permalink

I love working with grad students. I love it even more when they try to take forward steps despite uncertainty/confusion. Uncertainty/confusion is not just a part of learning or a sign of one’s early career stage, it’s a part of science. It does not go away.

Interesting - I wonder if it will have any real impact.

If you haven’t seen it, new NIH review criteria coming for Jan 2025. #neuroskyence#psychscisky#cogscigrants.nih.gov/grants/guide...

Follow my student @ldchurch.bsky.social who just joined Bluesky - she is the best!

I also hate it when they redo your figures. Multiple times I’ve minimized useless black (i.e., background) space around brain images, only to have the journal add black space in, so that now it’s 70% useless space and the actual data part is relatively smaller and thus lower resolution.

Clinical Science @ U Delaware is hiring for an open rank tenure-track position! We’re open to research expertise in any area within clinical science (broadly defined), although we’re particularly interested in those who focus on developmental processes. Please forward to any who might be interested.

Also, mean centering essentially reduces colinearity by assigning shared variance to main effects (although not perfectly).

Don’t you always want to assign common variance to the main effects, given that the interaction is not the product term itself, but the product with main effects partialed out (Cohen 1978, Psych Bull)? So any shared variance shouldn’t belong to the interaction.

Effect size of the interaction or the main effects?