Charles Driver

Sure yeah. Some domains are at least observationally rich enough with strong enough signal that it was clear well before ml. Curious how much further it can go.

That sounds like a 'not rich enough data' issue to me? what scenarios are there where this is a problem that better / more / different data can't resolve?

For some high enough value of 'well predicting', on a sufficiently rich dataset, they should converge at least! Interesting q to me is in which domains current available data is rich enough, and how we might determine that without explicit experiment...

as a re-evaluation / comparison / supplement I like it a lot, yeah. Feels like it makes the measurement problem even harder though, but will be interesting to see.

Why stop there? Why extract anything? Just put in the text and ask the llm what you want to know about the person / sample. (Bit sceptical here!)

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It would be easier to catch if we hadn't let in so much human slop.

Hard agree on attending to the (far more robust) contemporaneous correlations. But considering directed cross-effects *in combination with* the contemporaneous correlation also leads to much more consistent (and typically much less causally exciting) inferences. Rough draft:
osf.io/preprints/ps...

The (RI)-CLPM (discrete-time models in general) gets moderation structure really wrong if we include actual exercise as a mediator -- no, fitness shouldn't take 2 years to be influenced by motivation just because you ran a yearly panel study, or 2 weeks because you ran a weekly study.

An example I use is motivation to exercise. Allowing *some* time to pass between increasing motivation and observing meaningful changes in fitness is necessary. The time issue is much bigger in case of moderation structures though:

Congratulations.

I can be ok with using utilize but would draw the line at utilizing it.

So good to see!

Richard McElreath: It must not be overlooked that junior researchers DO NOT TRUST US. We, the directors, are a big part of the problem. We made this system, we remake it every year, and we benefit from it. What can we do to credibly signal our commitment to reform a corrupt research culture? My conversations with junior scientists in the society has taught me that directors are too often either indifferent or hostile to science reform. We cannot hope to convince our prize winning colleagues. Their egos are immune. But we can replace retirements with researchers who care more about integrity than their own prestige. This is important both for earning the trust of the junior researchers who really do the research in the MPG and for attracting excellent future directors and starting to earn the trust of the public. So I suggest two strong signals to our junior researchers (and the public): (1) we will reform recruitment and promotion at all levels to eliminate proxies like citation counts and journal brands in favor of reliability and sustainability; (2) we will make open science skills a core part of scientific training, through the graduate schools at a minimum, as conditions for the central funding. The most ambitious thing we could do, as hinted at in item 5 above, is to meaningfully invest in metascientific research. As the largest basic research organization in the world, the MPG is uniquely suited to studying research and its products from a broad perspective that includes the humanities, the sciences, and policy. Governments are already involved in science reform. Someone should study it in an organized and sustained way.

The Max Planck Society has begun an exploratory round table for open science. We are drafting some recommendations to leadership. Still a long way to go! But here are my notes on the most recent draft, just so you all know how I am trying to steer things.

Parametrization Cookbook: A set of Bijective Parametrizations for using Machine Learning methods in Statistical Inference arxiv.org/abs/2301.08297

UZH: Lecturer Research «Psychological Data Management and -Stewardship» The Department of Psychology at the University of Zurich invites applications for a tenured Lecturer Research position for «Psychological Data Management and -Stewardship». We are seeking an enthusias...

The Department of Psychology @uzh-ch.bsky.social has an open position for a tenured Lecturer (Research) «Psychological Data Management and -Stewardship»

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One way streets here are mostly only one way for cars. Obviously not a blanket solution for every case!

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The Australien Government has made an ad about the Social Media Ban for Under-16s, and it's surprisingly honest and informative.

You don't unfollow enough people?

Works the other way around too.

“If I was Jonathan Haidt, how would I destroy the next generation?”

I think without getting independence in there somewhere you suffer a similar problem with ESS (if every sample is effective then effective sample size is just n of that), but, nuance quibbling, the field has it's letters and I guess you've thought more in this domain than me :)

Since you make me think about it, if starting from scratch I would opt for something like ENIS -- estimated number of independent samples.

Nah I kinda love it that every time I'm forced to use a webmail client adding an attachment 'accidentally' uploads to onedrive, where I assume I've signed away the rights to my personhood or whatever already...

Best to have the talk early, you never know what they'll get up to...

Sure, but why is the respect always assumed to only flow in one direction? (To receiver). If my cultural background (bloody convicts) and time needs (needed) make skipping pleasantries seem favourable, shouldn't that carry some weight in a mutual interaction?

Honestly I'd be happy if we'd just name far less stuff and force people to describe what they really think they're doing...

A recent redesign of OSF by @cos.io led to widespread access failures. What began as a few broken download links became for me a total disappearance of eight years of DOI-registered work. What happened, how was it resolved, and what it reveals about trust and infrastructure in open science

Worth being aware of but also a mathy inevitability.

Now I'm also looking for a research software engineer to implement a pile of research results to R packages loo, posterior, bayesplot, projpred, priorsense, brms or/and Python packages ArviZ, Bambi and Kulprit. Apply by email with no specific deadline (see contact info at users.aalto.fi/~ave/)

That's not how it works in psych stats sorry 😆