Double-blind peer review is rare in my field but even if it wasn’t I don’t think it would be effective as it’s pretty easy in small fields to figure out the authors based on the research questions and methods alone
I recently got a peer review request with just an abstract and I was able to guess the authors, which was confirmed when I agreed and got full access to the paper
Preprints are getting pretty popular too, which make double-blinding pretty useless if you’re keeping an eye on preprints. The huge upside with preprints outweighs the loss of double-blind reviewing IMO
Making a paper double-blind friendly is just more work for authors, who are already spending hours of their time formatting their papers to adhere to journal style despite a good chance of rejection
I understand how double-blind review can help early career researchers, as they may help mitigate bias, but I think preprints can help more.
Best case benefit: Good feedback
‘Worst’ case: get your work out immediately so your CV isn’t filled with ‘Smith et al (submitted)’
• • •
Missing some Tweet in this thread? You can try to
force a refresh
And here's your annual reminder that a "study" cannot be underpowered, but rather, a *design and test combination* can be underpowered for detecting hypothetical effect sizes of interest towardsdatascience.com/why-you-should…
If you want to determine the range of hypothetical effect sizes a that given field can reliably detect (and reject), here's a demo with #Rstats code sciencedirect.com/science/articl…
My guide to calculating study-level statistical power for meta-analyses using the 'metameta' #Rstats package and web app is out now in AMPPS 🔓 doi.org/10.1177/251524…
Here's how this tool can be used for your next meta-analysis OR for re-analysing published meta-analyses 🧵
There's been a lot of talk recently about the quality of studies that are included in meta-analyses—how useful is a meta-analysis if it's just made up of studies with low evidential value?
But determining the evidential value of studies can be hard. Common approaches for looking at study quality or risk of bias tend to be quite subjective. You're not likely to get the same conclusions from different authors. These tasks can also be quite time consuming.
“Nearly 100% of published studies… confirm the initial hypothesis. This is an amazing accomplishment given the complexity of the human mind and human behaviour. Somehow, as psychological scientists, we always find the expected result; we always win!” royalsocietypublishing.org/doi/10.1098/rs…
One thing I want to add: the psychological sciences need to broadly adopt practices that help us determine when we're wrong. The standard NHST p-value approach cannot be used to provide support for absence of an effect. This paper provides two solutions academic.oup.com/psychsocgeront…
Strong auxiliary assumptions are also required for falsifying hypotheses
Participants who ate soup from bowls that were refillable, unbeknownst to them, ate 73% more soup than those eating from normal bowls pubmed.ncbi.nlm.nih.gov/15761167/
Oxytocin receptor (OXTR) expression patterns in the brain across development osf.io/j3b5d/
Here we identify OXTR gene expression patterns across the lifespan along with gene-gene co-expression patterns and potential health implications
[THREAD]
So, let's begin with some background.
As well as being an oxytocin researcher I'm also a meta-scientist, which means that a lot of my work on improving research methods is focused on improving oxytocin research (that's what got me into meta-science in the first place)
Earlier this year, we published a paper, led by @fuyu00, in which we proposed that three things are required to improving the precision of intranasal oxytocin research: Improved methods, reproducibility, and theory.