There’s nothing about those publications to brag about

Is it a bad time to tell you I had 12 publications upon graduation? That lab was trained in 🔪🥓

One thing to bear in mind is that people can postdoc here for a much longer time from what I know of the US, and this changes what the role is imo. There are even small numbers of tenured postdoc positions. There are many highly technical 7th+ year postdocs around.

Seconded for OS, and not even as a virtue: many PIs have OS expectations or requirements on them that they might not have the skills to fulfill alone, and rely on postdocs to drive forward.

I presume you mean that 5+ would raise eyebrows in a good way?

I think technical skills are in high demand, from what I see around Belgium/NL/German/Switzerland.

Standards are nowhere near as universal as the cohen ones. The {effectsize} R package collects and implements guidelines for interpretation, it’s a good go-to for a standard with references. I don’t know if my comment is in compliance with it. But the effect sizes are substantively very large.

Seems like they have a mix of SDs and SEs

Perhaps there are other inconsistencies - I only looked at PSS at post.

My calculator takes rounding into account and lets you explore if SE has been confused for SE - I think that’s likely here. It still comes out as d = 3.7 if you assume they are SEs though.

errors.shinyapps.io/recalc_indep...

Seems like there are inconsistencies in the summary stats repeated between Table 3 and Table 4. I think you’ve used those from table 4. When I use those from table 3 I get cohens d of about 21 (!)

You have huge signal boosting power, and I think that comes with the responsibility not to contribute to misunderstanding in this space. Every existing critique of this literature emphasises that causal claims are routinely made with weak or no causal evidence. Let’s learn from that.

I think you’re missing my point. We can’t squeeze causal claims out of longitudinal data, only inject causal assumptions. What we as readers can do is not repeat these causal statements as if they’re evidenced rather than assumed.

Surely our job is to critically evaluate research, not merely repeat authors' talking points.

Your opening tweet is a clearly causal claim that you've repeated, which immediately doesn't stand up to scrutiny. This is a ubiquitous critique of this type of work.