Guido Biele

wrote a bit fast, that's probably not an issue if one assumes the proportional odds assumtions holds and models the data accordingly, as done in the example.
If one relaxes the assumption and models category specific effects, things are probably different.

Very cool! I'm intrigued by the treatment of ordinal variables, and I have a question. Will this approach: avg_comparisons(mod_ord, variables = "partner", hypothesis = ~ I(sum(x * 1:5))) also give a good answer when the response-distribution is unbalanced, e.g. most people responded on level 1?

If you are not discouraged by now, here is a relevant paper: Bayesian Identification and Estimation of Growth Mixture
Models (Xiao, Rabe-Heskeseth & Skrondal, 2025) www.duo.uio.no/bitstream/ha...

The question is well motivated, but there will be stumbling blocks or walls one can run into:
- degenerate classes / label switching (betanalpha.github.io/assets/case_...)
- finding # of latent classes
- non-informative class membership probs

I think very clean data are needed to have a chance.

No worries, das Deutsch ist nicht so gut, als dass man leicht Unterhaltungen auf Deutsch führen könnte 😀. (Bin weder verwandt noch verschwägert mit dem Eigentümer...)

I recommend this places: maps.app.goo.gl/H2QkoDjDbJr8...
The owner speaks some German 😀. More importantly, it's a friendly and quiet place with reasonably priced very good meals and nice outdoor seating in the backyard.

I think a "rich-get-richer" effect could be stronger if one does not provide additional info. But that would in my view also be bad practice. Asking an LLM to help with a review by using a RAG system with the relevant literature could well make the effect weaker.

Genau für diese Art von Text, der in erster Linie für Verwaltungszwecke verfasst wird, sind m. E. LLMs nützliche und effiziente Werkzeuge, bei denen, aus meiner Sicht, die Vorteile ggü den Nachteilen der Nutzung überwiegen.

Generally agree. An neat use of ordinal regressions is to use them as a semi-parametric model for weird (multimodal) VAS data. If one splits the original scale in bins, one can with some effort calculate marginal effects on the original scale, with resolution depending on # of bins.

Typos in the post are virtue signals, indicating that no LLM was used in its writing 😀

The last few days a couple of texts made the round here that equate using AI in the research process with doing bad research. I think these are 2 orthogonal things: Using AI can make research worse and it can make it better, depent on how it is used.

I don't see how the post effectively criticises target population estimands. It shows that if you have a target population estimand in mind, but your study isn't designed to estimate it, you'll pay with a high variance of your estimate.

OTH, “Our analysis should take into account that children grow around 7.5 cm per year and rarely less than 1 cm or more than 15 cm per year.” sounds entirely reasonable and illustrates that priors often reflect not just subjective beliefs but objective information that can robustify inference.

One reason for the lack of appreciation might be that Bayesian methods are often taught in a very abstract way, just as in the book at the start of the thread. Teaching the formula “posterior = prior × likelihood” tends to focus attention on so-called subjective beliefs as priors.

I agree that one can use Bayes for many reasons and that it is useful in relatively simple situations. However, my (admittedly limited) observation is that the ability to fit complex models attracts more people to Bayesian estimation than improved inference in simpler cases.

It works usually well if one uses t-tests or anovas to analyse simple experiments. But one shouldn't generalise from that to the goals/usefulness of Bayesian or Frequentist methods in general.

It seems to be a common misunderstanding that the goal of applied Bayesian inference is to let the prior influence the inference. I think a more common goal is to be able to reliably fit complex models that are hard/impossible with Frequentist methods.

Did they look at both parental income and education? In Norwegian data the association of child outcomes with parental education is clearly stronger than with parental income.

To let LLMs produce less common and therefore likely also more creative text, one can always set the temperature parameter.
But there are trade offs. Less common might also be more likely wrong.

Sometimes psychologists collaborate with causal inference experts and use it for their research, here the probability of benefit or harm of ADHD medication for school outcomes: arxiv.org/abs/2502.10049
All math by @johandh2o.bsky.social

YouTube video by 3Blue1Brown Transformers (how LLMs work) explained visually | DL5

Understanding the transformer architecture helps explain why LLMs are so powerful—and when they’re likely to fail.
The Andrej Karpathy video goes into less detail about transformers than would be helpful. Here's a quick and accessible intro from 3Blue1Brown. www.youtube.com/watch?v=wjZo...

I believe large language models (LLMs) can be very useful and can be used to support learning effectively.
However, this plot from a recent study (www.nature.com/articles/s41...), which praises the impact of LLMs on learning outcomes, doesn't exactly inspire confidence.

Easily only for models with identity link function (also careful with treatment-interactions). Otherwise one might have to code up a model in e.g. Stan, omit priors on coefficients involving the treatment & put a prior on the marginal effect (calculated in the transformed parameters block).

The article talks about non-collapsibility, but doesn't seem to use the term. I think its a helpful term, because it puts a name to a key problem with OR, which is there even if the OR is not interpreted as a RR.

Links:
documents1.worldbank.org/curated/en/0...
assets-eu.researchsquare.com/files/rs-424...
arxiv.org/pdf/2409.090...
arxiv.org/pdf/2402.09809

Would be great to have that on CRAN 😀

Ist natürlich auch mit Vorsicht zu geniessen. Eine wichtige Schwäche ist, dass es alle Aussagen von Autoren unkritisch akzeptiert.

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Genau, wenn schon, dann besser elicit.com :-)

Anyhow, I am sympathetic to focusing on selection bias, because I think quasi-experimental designs, which are often possible only in selected samples, focus so much on internal validity, that it easy to overlook that they might exchange confounding bias with selection bias.

Using missingnes graphs provide an additional way to define internal and external validity: violations of internal validity are due to backdoor paths that do not involve missingness indicator nodes (or their children) and violations of external validity involve those. (Just made this up ...)