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Daniel 🕹️

@strengejacke.de

He/she/it - 's' muss mit. We're lower than the world! R easystats project: https://easystats.github.io/easystats/

507 Posts624 Follower111 Folgt
Daniel 🕹️@strengejacke.de · 2mo ago

Curaçao fans were in a fantastic mood even after their defeat against Germany—and then, the German Ballermann music started playing in Houston 🙈 Horrible...

Daniel 🕹️@strengejacke.de · 3mo ago

The biggest hurdle in switching from SPSS to R is thinking in programming logic. Instead of single datasets or temporary outputs, R treats everything as an object. To master this mindset, I highly recommend Andy’s video series - especially "Creating an Object" and "What is a function?" #rstats

Prof Andy Field@profandyfield.com · 3mo ago

There's also `creating objects in R`, but that one doesn't involve cake and I look really misreable in the thumbnail. youtu.be/i894fWOkmEw

Daniel 🕹️@strengejacke.de · 3mo ago

Even if you don't like Bayes factors, if you read this, you'll like them! #easystats #rstats #bayestestR

Mattan S. Ben-Shachar@mattansb.msbstats.info · 3mo ago

🚨NEW-ish VIGNETTE ALERT🚨 The Bayes factor vignette for {bayestestR} has been completely re-written! We've got all the BFs! - Marginal likelihoods - Model & posterior averaging - Order restrictions - Savage-Dickey density ratios AND MORE! Read it up, here: @easystats.github.io #Bayes #rstats

Andrew Bell@andrewjdbell.bsky.social · 10mo ago

Want to know how to use MAIHDA to examine heterogeniety in policy outcomes? Here's a nice worked-through example! How does intersectional identity impact preference for in-person vs online GP appoinments? Has been great working with @healthfoundation.bsky.social colleagues on this :)

A caterpillar plot of slope variability in the effect of appointment type. Statistically significant differentials are coloured in blue, of which there are 4 positive and 8 negative. The figure is taken from the linked blog figure 5
Charles Tallack@charlestallack.bsky.social · 10mo ago

Intersectionality describes how demographic disadvantages can interact to amplify inequalities. But how to model this? New from colleagues in our analysis team: a case study of GP appmts & patient experience, illustrating the MAIDHA intersectional modelling approach. medium.com/healthfdn-da...

Daniel 🕹️@strengejacke.de · 11mo ago

Is there a term for this kind of approach, which maybe also include "simpler" analyses, like the general use of EMMs or comparisons and alike? I always use "pairwise comparisons" as a synonym, because that is what more people know. What I'm actually referring to is a "post-estimation framework" /1

Julia M. Rohrer@dingdingpeng.the100.ci · 12mo ago

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).