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Posts by Tjeerd Rudmer de Vries

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Day 2 of #ecdp2025 has kicked off with the keynote of Prof. Livio Provenzi on behalf of the DPB lab!

#EADP #devpsy

7 months ago 3 2 0 0
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LatentGOLD 6.1 Academic New License - Statistical Innovations LG 5.1 Academic Latent GOLD new license LG Choice Advanced Syntax Latent Class Discrete Choice 3step markov multilevel scale factor 5.1 annual perpetual

🤓Latent GOLD licenses for academic use are now freely available on the website! www.statisticalinnovations.com/shop/lg-6-1-...

1 year ago 9 2 1 0

Great stuff! Thanks for adding me. Can you also add @jetteechterhoff.bsky.social?

1 year ago 0 0 1 0

That’s great! Thanks for setting it up. Could you add both me and @jetteechterhoff.bsky.social ?

1 year ago 0 0 0 0

Could you maybe also add @jetteechterhoff.bsky.social ?:)

1 year ago 1 0 1 0

Thank you! 😊

1 year ago 0 0 0 0

Thank you for creating this! I'd love to be included too! :)

1 year ago 1 0 1 0
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Reflexivity in quantitative research: A rationale and beginner's guide Reflexivity is the act of examining one's own assumption, belief, and judgement systems, and thinking carefully and critically about how these influence the research process. The practice of reflexiv...

Reflexivity in quantitative research: A rationale and beginner's guide (2023)

compass.onlinelibrary.wiley.com/doi/10.1111/...

1 year ago 37 19 1 0
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Idiographic Approaches in Psychology: Hold your horses There have been persistent calls spurring psychologists to do more “idiographic” research, starting even before Peter Molenaar’s “Manifesto on Psychology as Idiographic Science”, which is already 20 y...

New blog post! Three reasons why (some) idiographic studies in psych leave me unimpressed: (1) failure to establish that there's meaningful variation, (2) imprecise estimates at the individual level, (3) the usual estimand confusion (is it causal? yes/no/maybe a bit?).

www.the100.ci/2024/10/28/i...

1 year ago 67 27 7 3

I have a joke about experimental social psychology, but it doesn’t work if you repeat it.

1 year ago 361 67 3 22

Gonna be provocative again:

Almost all factor models are made up of formative indicators

#stats

1 year ago 6 1 5 0

I advocate for critical thinking about what “SES” is supposed to represent. Social theory & quantitative sociology provide good ways of doing this before selecting variables. Is it social status, ie. prestige you want to measure? Or social class? Deprivation? Each have different measures

1 year ago 17 9 2 0
Understand and Describe Bayesian Models and Posterior Distributions Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Krus...

New update to {bayestestR} expands support for a tidy workflow -
working better with tidy inputs, `rvar`s, and post-modeling estimates, and generating tidy outputs!

@easystats.bsky.social #rstats

easystats.github.io/bayestestR/

1 year ago 23 10 1 1

Great, thank you for setting this up! Could I be added to the list? I am working on lifecourse mental and physical health, with particular focus on early life determinants

1 year ago 1 0 1 0

Differential effects of childhood maltreatment types and timing on psychopathology in formerly out-of-home placed young adults: http://osf.io/2vrjt/

1 year ago 1 1 0 1
An open letter regarding Scientific Reports 16th October 2024  to: Mr Chris Graf Research Integrity Director, Springer Nature and Chair Elect of the World Conference...

Open letter from a group of sleuths re problems with editors/papers at Scientific Reports
deevybee.blogspot.com/2024/10/an-o...
They've published 23K papers in 2024 with APC of £2090, raising £48 million from this journal alone, so should be able to put resource into cleaning this up.

1 year ago 91 70 3 12

Hard to publish null results, academic prizes (e.g. Nobel) only for the spectacular results, late breaking sessions on science congresses for surprising findings with impact, … and we keep being surprised when hear news about scientists exaggerating results and fake their data

1 year ago 39 10 0 3
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Screenshot of Sky Follower Bridge in action - identifying three people who I was following on Twitter with a matching account on BlueSky

Screenshot of Sky Follower Bridge in action - identifying three people who I was following on Twitter with a matching account on BlueSky

For those moving from Twitter to Bluesky the #SkyFollowerBridge extension for Chrome and Firefox is a very easy way to find people who you followed on Twitter.

Download and run the extension with your Twitter 'following' page open, and it finds suggested matches to follow (with a click) on Bluesky!

1 year ago 58 32 3 16
Initiatives that promote mental well-being are formally recommended for all British workers, with many practices targeting change in individual workers' resources. While the existing evidence is generally positive about these interventions, disagreement is increasing because of concerns that individual-level interventions do not engage with working conditions. Contributing to the debate, this article uses survey data (N = 46,336 workers in 233 organisations) to compare participants and nonparticipants in a range of common individual-level well-being interventions, including resilience training, mindfulness and well-being apps. Across multiple subjective well-being indicators, participants appear no better off. Results are interpreted through the job demands–resources theory and selection bias in cross-sectional results is interrogated. Overall, results suggest interventions are not providing additional or appropriate resources in response to job demands.

Initiatives that promote mental well-being are formally recommended for all British workers, with many practices targeting change in individual workers' resources. While the existing evidence is generally positive about these interventions, disagreement is increasing because of concerns that individual-level interventions do not engage with working conditions. Contributing to the debate, this article uses survey data (N = 46,336 workers in 233 organisations) to compare participants and nonparticipants in a range of common individual-level well-being interventions, including resilience training, mindfulness and well-being apps. Across multiple subjective well-being indicators, participants appear no better off. Results are interpreted through the job demands–resources theory and selection bias in cross-sectional results is interrogated. Overall, results suggest interventions are not providing additional or appropriate resources in response to job demands.

Today is World Mental Health Day, and the theme this year is Mental Health At Work.

So it's a good time to share this study (N = 46,336), which suggests that individual-level MH interventions at work are not effective at improving wellbeing

onlinelibrary.wiley.com/doi/10.1111/...

1 year ago 17 12 3 2
The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization
Abstract: 
Recent developments in the causal inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do we actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and non-interference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses...

The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization Abstract: Recent developments in the causal inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do we actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and non-interference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses...

New preprint! There are lots of interesting estimators that can be used to target causal effects, some of which aren't well-known in psychology. Arthur Chatton and I provide a (gentle) introduction to approaches commonly used in epidemiology: psyarxiv.com/k2gzp

2 years ago 72 32 4 4