Vlad Krivoshchekov
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vladkri.bsky.social
Vlad Krivoshchekov
@vladkri.bsky.social
35 followers 49 following 15 posts
PhD in Social Psy. Masculinities, diversity, language & social cognition. (he/him) 🏳️‍🌈🇨🇭 opinions my own
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Reposted by Vlad Krivoshchekov
scientificdiscovery.dev
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...
screenshot of my post
December 9, 2025 at 8:28 PM
Reposted by Vlad Krivoshchekov
syeducation.bsky.social
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 of the article, "Psychology wants to stay WEIRD, not go WILD"
November 13, 2025 at 2:59 PM
Reposted by Vlad Krivoshchekov
casperalbers.nl
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...
screenshot from the paper, stating that no causal claims (like they did in the title) should be made. paper title
November 11, 2025 at 7:58 AM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
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?...
October 21, 2025 at 3:16 PM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
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...
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.
August 25, 2025 at 11:49 AM
Reposted by Vlad Krivoshchekov
lakens.bsky.social
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_...
August 25, 2025 at 4:54 AM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
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
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.
August 15, 2025 at 12:12 PM
vladkri.bsky.social
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/...
August 4, 2025 at 12:49 PM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
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...
What’s in a correlation?
Correlation may not imply causation, but let’s just ignore that for a second. Correlations are standardized effect size metrics and as such have some quirks by design. These are benign enough when you...
www.the100.ci
July 28, 2025 at 12:21 PM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
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!
Infographic illustrating different ways to model age. First panel shows two "extreme" cases; including age as a linear numerical predictor (df = 1) or including age as a categorical predictor (df = number of years of age minus 1). Second panel shows an intermediate solution in which age is categorized into broader bins (df = number of categories minus 1, here 5 - 1 = 4). Third panel shows an intermediate solution in which age is included with a polynomial (df = degrees of freedom of the polynomial, here 4). Fourth panel shows an intermediate solution in which age is modeled with the help of splines (df = degrees of freedom of the splines, here 4).
July 16, 2025 at 12:33 PM
Reposted by Vlad Krivoshchekov
lastpositivist.bsky.social
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.
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.
June 14, 2025 at 8:28 AM
Reposted by Vlad Krivoshchekov
pbogdan.bsky.social
🚨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
Figure 1 of the paper
April 9, 2025 at 1:15 PM
Reposted by Vlad Krivoshchekov
mckeever.bsky.social
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.
May 24, 2025 at 11:05 AM
Reposted by Vlad Krivoshchekov
georgealfredcurrie.bsky.social
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/...

#OpenScience
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.
May 19, 2025 at 2:52 PM
Reposted by Vlad Krivoshchekov
forrt.bsky.social
🎓 Get PAID to co-create open-access teaching materials!

We're launching a funded initiative to make open science education more inclusive and accessible beyond elite institutions and the Global North.

Contribute a set of materials and earn €400. 🧵
May 17, 2025 at 3:23 PM
Reposted by Vlad Krivoshchekov
davidbaranger.bsky.social
I also somewhat recently added a function to additionally include these interactions in my interaction power analysis package: dbaranger.github.io/InteractionP...
Interaction Power: Power analyses for 2-way interactions with covariates
This article describes how to use InteractionPoweR to run power analyses for 2-way interactions that also include covariates
dbaranger.github.io
May 11, 2025 at 5:32 PM
Reposted by Vlad Krivoshchekov
radboudgender.bsky.social
Interested in how how migration reshapes fatherhood, emotional life, and gender performances?
@klararaiber.bsky.social and Francesco Cerchiaro are hiring a PhD candidate for an exiting project at Radboud University.

Find all information here: www.ru.nl/en/working-a...

Deadline: June 1st
PhD Position: Fatherhood, Emotions and Masculinity in Migration | Radboud University
Do you want to work as a PhD Position: Fatherhood, Emotions and Masculinity in Migration at the Faculty of Social Sciences? Check our vacancy!
www.ru.nl
May 7, 2025 at 2:44 PM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
Great example for a health behavior where people will be like “everybody does X, it’s never going to change” until it actually does change within a rather short time span, from a historic perspective.
scientificdiscovery.dev Saloni @scientificdiscovery.dev · Apr 21
Once widespread, smoking is now uncommon in Great Britain 🧵
Line chart showing the decline in smoking among adults over age 16 in Great Britain from 1974 to 2023. In 1974, around 51% of men and 41% of women reported smoking cigarettes. Over the decades, these rates fell steadily. By 2023, only about 12% of men and 10% of women reported smoking. The gap between men and women remained fairly consistent, with men having slightly higher rates throughout the period. The chart illustrates a dramatic and sustained decline in smoking for both sexes. The data is sourced from the GLS & OPN surveys (2023) and published by OurWorldinData.org under a Creative Commons BY license.
April 21, 2025 at 9:04 AM
vladkri.bsky.social
New paper!

Using three independent samples of Russian men collected before and after the so-called mobilization in 2022 and in winter 2024, we show that the more strongly men endorse traditional masculinity ideology, the more they support state-sanctioned military actions — also war crimes.
April 8, 2025 at 5:44 AM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
Looking forward to another iteration of my „Everything is Causal Inference“ talk, this time at the Hertie School of Governance in Berlin, courtesy of @conjugateprior.org. This is probably the most opinionated talk from my œuvre, so I guess I will have to write it down at some point...
osf.io/43sn7
March 18, 2025 at 1:45 PM
Reposted by Vlad Krivoshchekov
quentinandre.bsky.social
Psych. Science just published a paper showing that the "warm glow" of donations can reduce depressive symptoms (d = [0.19, 0.46]).

Most impressively, "micro" donations (one Chinese cent, or $0.0014) appear sufficient to trigger these large effects.

Big if true? Let's dive in.
March 11, 2025 at 6:05 PM
Reposted by Vlad Krivoshchekov
eikofried.bsky.social
1/ What do people perceive as causes for depression, poverty, & mortality—and are social or non-social causes more important?

New preregistered study led by Emma Bridger in a n=1000 UK sample, including a wide variety of perceived causes.

#episky #psychscisky #PublicHealth 🧪
February 27, 2025 at 2:57 PM
Reposted by Vlad Krivoshchekov
dingdingpeng.the100.ci
New guest post! Excluding "careless" responses is a routine practice in many contexts. But if careless responding itself is an outcome of the variables of interest, that may actually cause trouble.
In this scenario, we are interested in the effect of X on Y. However, careless responding is affected by Y. If we exclude respondents who show signs of careless responding -- and thus condition on this node -- we potentially bias our estimate of the effect of X on Y.
February 18, 2025 at 1:01 PM
Reposted by Vlad Krivoshchekov
lakens.bsky.social
We love to share new papers on here. But how many of the studies that scientists preregister on the Open Science Framework are never shared publicly? In a new paper in AMPPS we estimate 40% of preregistered studies are never shared. That’s a lot. journals.sagepub.com/doi/10.1177/...

🧵⤵️
An Inception-Cohort Study Quantifying How Many Registered Studies Are Publicly Shared - Eline N. F. Ensinck, Daniël Lakens, 2025
We quantified how many studies registered on the OSF up to November 2017 are performed but not shared after at least 4 years. Examining a sample of 169 register...
journals.sagepub.com
January 29, 2025 at 4:36 AM
Reposted by Vlad Krivoshchekov
koenfucius.bsky.social
Ill-defined, poorly understood, hard to measure and often used to dismiss small but significant effects (especially when they’re inconvenient). @orbenamy.bsky.social and @lakens.bsky.social review the ‘crud factor’ and propose ways to address its problems: https://buff.ly/4jyrhXq
January 27, 2025 at 4:09 PM