I’ve now realized that LLMs are to statisticians what WebMD is to physicians.

There’s a lot of good stuff like this from Sarah Depaoli, @winterstat.bsky.social and Rens van de Schoot

Here’s a really good paper on the topic my grad advisor is on:

www.tandfonline.com/doi/abs/10.1...

It really was. I was amazed that Clarke created just as huge of a world in Piranesi while using only a fraction of the pages of Jonathon Strange

Clarke is one of my favorites authors. I recently read both Piranesi and Jonathon Strange & Mr Norrell with my kid

In Sacramento, we would go to the nimbus fish hatchery during salmon season, the jelly belly factory, maidu museum, sutter’s fort, sutter’s mill, and sly park

I’ve found it particularly enlightening, and maybe disheartening, that you can specify a CFA to give you the sample mea and provide fit indices for it…

I recently spent some time explaining INUS conditions to social and medical scientists to try to dispel this.

Great write up of a really cool loneliness study from @kvanbogart.bsky.social

He’s sleepin’ on my couch

Some recent shots of me presenting work on how bidirectional associations between loneliness and cognitive performance change over time using Bayesian estimation at the @geronsociety.bsky.social for the Center for Healthy Aging @pennstateuniv.bsky.social

Super cool new study by @kvanbogart.bsky.social looking into differences between chronic and acute loneliness!

I think the issue is that we can’t know if it’ll give the same results, in our specific use case, until after doing all that work, which also sucks.

I remember the tabachnick and fidell book “experimental designs using ANOVA” that I used in my MA program being super helpful, but it’s been a while since I looked at it.

I learned in grad school to not rely on p-curve analyses from Will shadish and Jack Vevea. I was surprised to see them used so frequently after I graduated, but I could never find a paper backing up what I was taught until now!

Oh I see! Then I also agree lol

Which is why the causal inference we work with is probabilistic and not deterministic

I’m with @solomonkurz.bsky.social . Is this how the field is looking at causal inference? The causal inference class I took in grad school and the ones I teach focus on how we typically only have INUS conditions.

Take a look at our new pre print for different approach on how to calculate the dynamic range of diurnal cortisol, led by the talented @kvanbogart.bsky.social

All this! I just did a causal inference workshop at Penn State and I started off with “stats can’t save you here. Causal inference is a qualitative inference given the data collected and the design of your study.” It was very Shadish, Cook, and Campbell heavy with a dash of DAGs

This book was so good! Have you read jonathan strange and mr norrell yet?

The tabachnick and Fidell book, analyzing experiments using ANOVA. has a nice section on a priori contrast for one way and factorial ANOVAs. They even include a table at the end for different contrast based on the number of groups you have. Linda collins MOST book is a good one too

If MCAR, I’ll just randomly impute 1s and 0s so I don’t lose power

I will be damned if I allow a bunch of Confederate-waving January 6th apologists give the American people a lecture on flag waving.

There is ZERO reason to enter an argument about patriotism with people who still worship traitors to America 150+ years later.

They. Are. Breaking. The. Law.

Oh absolutely. Unfortunately it means you’ll have to double check these errors in lme4 before determining if they are erroneous though

Another thing to request, particularly when using lme4 or nlme in R, is the confidence intervals. If you just do a summary on the model object, you won’t actually know if your model has converged, particularly in the random effects. Asking for confint (in lme4) or intervals(in nlme) will check this

As a Sacramento kings fan, I am a fan of anyone that is a thorn in the Lakers side. Go Celtics!

📅 Save the date – May 14 @ 1:30 p.m.! Join us for our BERD Workshop Webinar: “Causal Inferences for Experimental and Quasi-Experimental Designs” |✅ Reserve your spot: bit.ly/4jB2EIU