"Why would some highly-skilled emigrants opt to return to their autocratizing countries, even if they are concerned about the path their country has taken?" My latest article looks into this puzzle and conceptualizes "return migration as voice": onlinelibrary.wiley.com/doi/10.1111/... #academicsky
John Oxley points out the clear yet often overlooked point that young men 18-24 are the second most liberal, left-leaning group in society (after young women) - being more so than young-ish thirtysomething women, and older women, as well as than men in older cohorts
Do you sometimes think "oh boy, I would really like to hear Julia ramble some more about the topics about which she doesn't stop talking to begin with?"
The wait is finally over! @guruspod.bsky.social and had a chat about open science, causal inference, and apparently birth order effects.
Figure 1 from the paper. Econ: from 2008 to 2024, methods aiming for causal inference have increased, theoretical work has decreased. Psych: Mostly experimental or descriptive correlational work.
Just learned about this study looking at methodological trends in psych and econ over time: online.ucpress.edu/collabra/art....
Matches my perception well: Nobody in psych bothers to (explicitly) try causal inference unless they conducted an experiment, not a lot of theoretical work either.
An abbreviation (ABB) in a journal article (JA) or Grant Application (GA) is rarely worth the words it saves. Every ABB requires cognitive resources (CR) and at my age by the time I'm halfway through a JA or GA I no longer have the CR to remember what your ABB stood for.
Congratulations!
screenshot of my post
Big new blogpost!
My guide to data visualization, which includes a very long table of contents, tons of charts, and more.
--> Why data visualization matters and how to make charts more effective, clear, transparent, and sometimes, beautiful.
www.scientificdiscovery.dev/p/salonis-gu...
A quick (1000 words) read to enjoy with your morning coffee or afternoon tea:
"Psychology wants to stay WEIRD, not go WILD"
Why hasn't psychology diversified it samples, methods, theories, etc.? Because it doesn't want to. osf.io/preprints/ps...
screenshot from the paper, stating that no causal claims (like they did in the title) should be made.
paper title
Doing non-causal inference (and being explicit about it), yet using a causal word as second word in the title.
If you pay Nature € 10.690, they will publish this in Nature Ageing.
I can tell you what I think of that for free.
www.nature.com/articles/s43...
The recording is now available so that you can confirm that I indeed have a German accent and color-match my outfits with my Zoom background.
youtu.be/YL0co26ng-g?...
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).
Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.
A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
Ever stared at a table of regression coefficients & wondered what you're doing with your life?
Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
If you are preparing your bachelor statistics course and would like to add optional material for students to better understand statistics on a conceptual level (see topics in the screenshot) my free textbook provides a state of the art overview. lakens.github.io/statistical_...
Nonparametric Causal Decomposition of Group Disparities Ang Yu, Felix Elwert We introduce a new nonparametric causal decomposition approach that identifies the mechanisms by which a treatment variable contributes to a group-based outcome disparity. Our approach distinguishes three mechanisms: group differences in 1) treatment prevalence, 2) average treatment effects, and 3) selection into treatment based on individual-level treatment effects. Our approach reformulates classic Kitagawa-Blinder-Oaxaca decompositions in causal and nonparametric terms, complements causal mediation analysis by explaining group disparities instead of group effects, and isolates conceptually distinct mechanisms conflated in recent random equalization decompositions. In contrast to all prior approaches, our framework uniquely identifies differential selection into treatment as a novel disparity-generating mechanism. Our approach can be used for both the retrospective causal explanation of disparities and the prospective planning of interventions to change disparities. We present both an unconditional and a conditional decomposition, where the latter quantifies the contributions of the treatment within levels of certain covariates. We develop nonparametric estimators that are n‾√-consistent, asymptotically normal, semiparametrically efficient, and multiply robust. We apply our approach to analyze the mechanisms by which college graduation causally contributes to intergenerational income persistence (the disparity in adult income between the children of high- vs low-income parents). Empirically, we demonstrate a previously undiscovered role played by the new selection component in intergenerational income persistence.
Just finished reading (the non-technical parts of 😋) this paper by @ang-yu.bsky.social and Felix Elwert. This is conceptually really cool stuff that may also be of interest to psychologists working on group differences, so here's a short 🧵 with my understanding of it:>
arxiv.org/abs/2306.16591
This is not about being “nice.”
It’s about confronting a masculinity culture that undermines care, collaboration, and change.
Engineering students want a different culture. We need to help build it.
What struck me most? For many students, this was one of the first times they were both asked and given space to reflect on their emotions. That space is painfully absent from most engineering curricula; yet students long for it.
When asked what they missed most at the end of the project, most students didn’t mention more feedback or better tools. They said: “connection.”
They wanted more time and space to connect—not just perform (altough performance was important).
Students challenged these norms:
- They openly voiced emotional needs and valued emotional expression (negative and positive alike).
- Emotions were used to build trust.
- Caring for each other became part of how they worked. Sometimes through small nonverbal cues, sometimes through direct support.
The result? Many students reported lower satisfaction with their learning experiences, increased stress, and weaker team cohesion.
And yet, we also saw resistance.
- They felt pressure to suppress both negative and overly positive emotions to “look confident.” expressing emotions meant to be perceived as weak.
- Informal gendered practices (“guys deciding things in (male) bathrooms”) excluded others, and students thought that it is just how things are.
Together with Nihat Kotluk, Yoann Favre, Fiori Marina, Egon Werlen , and Roland Tormey, we explored this question. We invited students at two Swiss technical universities to work in teams over six weeks to develop a language-learning app. And we interviewed them about their emotional experiences.
But how do these norms actually shape students’ learning experiences, especially when working in team projects meant to mirror their future professional experiences?
Engineering culture is often driven by norms of competition, individualism, and emotional restraint. That’s hardly news. And that engineering education reproduces these norms? Also not surprising.
Our recent research where we show how engineering students in team projects enact hegemonic and counterhegemonic practices through their emotional experiences and how it affects learning and challenges/reproduces wider masculinity culture is now online!
onlinelibrary.wiley.com/doi/10.1002/...
New blog post! Calculating a correlation is easy enough. But let's say you calculated two of them and they happen to differ. What follows from that? Turns out there are too many moving parts for an easy answer.
www.the100.ci/2025/07/28/w...
At this point, I might as well --
Here's an infographic showing different ways to include age as a predictor. The top shows two extremes, just as a plain old numerical predictor (imposes linear trajectory) vs. categorical predictor (imposes nothing whatsoever). And then three solutions in between!
Jury Theorems for Peer Review Marcus Arvan, Liam Kofi Bright, and Remco Heesen Abstract: Peer review is often taken to be the main form of quality control on academic research. Usually journals carry this out. However, parts of maths and physics appear to have a parallel, crowd-sourced model of peer review, where articles are posted on the arXiv to be publicly discussed. In this article we argue that crowd-sourced peer review is likely to do better than journal-solicited peer review at sorting articles by quality. Our argument rests on two key claims. First, crowd-sourced peer review will lead on average to more reviewers per article than journal-solicited peer review. Second, due to the wisdom of the crowds, more reviewers will tend to make better judgements than fewer reviewers will. We make the second claim precise by looking at the Condorcet jury theorem as well as two related jury theorems developed specifically to apply to peer review.
Paper is finally up and open access (www.journals.uchicago.edu/doi/10.1086/...), it's a sequel to an earlier paper where we'd argued that there's not good evidence that pre-publication peer review is a net benefit (www.journals.uchicago.edu/doi/10.1093/...). So in this one we suggest an alternative.
Figure 1 of the paper
🚨New paper!🚨
Meta-analysis on 4M p-values across 240k psych articles: How has psychology changed since the replication crisis began? How is replicability linked to citations, impact factor, and university prestige? 🧵
Paper: journals.sagepub.com/doi/10.1177/...
Interactive: pbogdan.com/meganal
Normalise writing a nice quick note to scholars literally every time you read and like their work. Our world is small and getting smaller. We need encouragement.
Screenshot of article summary of: Toward Science-Led Publishing by Damian Pattinson, George Currie published as an opinion piece, in Learned Publishing Summary The current dynamic of scholarly publishing prioritises the wants of the publishing industry over the needs of the research community. This article explores this theme through the lens of ‘publisher-led science’ as a description of our current status quo, and through ‘science-led publishing’ as an improved future state. We argue that financial motivations central to most publishing distort how research is presented, how it is assessed and even what research is undertaken, leading to a system that hinders, rather than facilitates, scientific progress. We propose three elements of a science-led publishing approach that would accelerate research communication, incentivise collaboration between authors, editors and reviewers, and create a more transparent and equitable research landscape. We believe that research funding and research assessment are two of the primary levers for wider change in research and research culture and consider the future purpose of scholarly publishing in a world where these proposals have been widely adopted.
Does publishing serve science or is science serving publishing?
Damian Pattinson and I (@elife.bsky.social) argue scientific publishing has evolved into a system that, rather than facilitate scholarly communication, distorts and dictates it.
onlinelibrary.wiley.com/doi/10.1002/...
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