Sean Mackinnon

I wonder what personality psychologists think of this analysis on.ft.com/45G0aEF by @jburnmurdoch.ft.com (which updates this paper from a couple years ago doi.org/10.1371/jour...)

That said, as a teacher the student population has changed a lot since 2020. I have twice as many registered accomodations, about 20% more absentees from class.

So, it definitely seems like *something* has changed in terms of the things measured by C and N, but hard to say if it's personality.

The one major thing that is missing is a test of measurement invariance.

There is also evidence that perfectionism has been increasing over time, so you could see this pattern b/c the reference for "average" C is changing in the minds of young people to be higher (e.g., scalar invariance)

One of the clearest papers I ever read on the subject is this one, which might be useful to you

uopsych.github.io/psy611/readi...

or is PCA only factor analysis if it comes from the facteur region of psychology

Submitting a journal article and I had to:

Include an abstract in the paper without track changes
Include a version of the abstract with track changes
Add a separate file that has just the abstract (again)
Then type the abstract in a separate window

Surely, efficiencies could be implemented

Sorry, actually more annoyingly calls it:

d*s (just above equation 15)

But yeah, I'd just have a subscript t in there I think, in addition to just footnoting the formula.

Hmm, well the first annoying thing is that even with regular SD, there are multiple denominators in use, so you have to indicate that too. I think Lakens refers to the first equation as:

ds (subscript s)

So I guess I'd use that, but add a "t"

dts

orbilu.uni.lu/handle/10993...

Could you clarify what is in the denominator of the SMD fraction when you talk about "pooled scale"?

In the paper you cited he writes this as the formula, which is a pretty standard usage of the standard deviations, so you must mean something else

I think that'd be "enhanced" in this typology? But yeah, definitely what a lot of ppl use it for

I kind of like this method of acknowledging AI use in published works. It's not perfect, and naturally relies on honesty, but so does all academic work. I think it'd be a useful way to have a discussion with students about its use.
aicontributionscale.my.canva.site/acknowledgeai

I usually type my papers single spaced because it's easier to read and write, and only double space at the end once I have to submit it

Cohen's ds (attached) is annoying arithmetic, and doesn't jive with the Welch t-test because it assumes equal variances.

Cohen's d_rm for the paired t-test requires knowing what a correlation is first, which comes later in the curriculum.

Cohen's dz, t/SQRT(N) isn't comparable to between subjects

Solved a minor teaching snag for intro #stats. I teach the Welch t-test and paired t-tests and wanted to teach cohen's d as an effect size measure. But ... what goes in the denominator for both?

Best formula for intro teaching is this, which works for BOTH:

d = (M1 - M2) / SQRT((S1^2 + S2^2)/2))

Virgin non-parametric vs Chad linear regression Source: https://www.reddit.com/r/virginvschad/comments/c4xdxg/virgin_nonparametric_vs_chad_linear_regression/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

From r/virginvschad 👀
#stats

So, what is void's deal? Is it a kind of chatbot, a person impersonating a chatbot? Something else?

Picture shows Shiny app where a user has selected a cut-off value for person-total correlation do divide a distribution into cases that are flagged as possible random responders, or spared. Classification metrics (accuracy, sensitivity, specificity) at the given cut-off are shown after the user clicks "Classify!"

If you do survey research with Likert-type items and worry about random responders, Michael John Ilagan & I made a Shiny app we think some of you may like.
falkcarl.shinyapps.io/BotApp1/

#SurveyResearch #DataQuality #Psychometrics #OnlineSurveys #SurveyBots #RandomResponders #rstats #Shiny

I feel like "augment" is doing a lot of heavy lifting in that analysis.

Like in teaching, AI-proofing assessments is technically augmented work, but really just more work that didn't previously need to be done

Ohhh this is awesome, I am definitely going to use this .gif to explain to my students what a loess is doing in geom_smooth() for my students

One of the things that keeps me from going totally crazy is knowing that AI models are currently in the "be cheap and good to customers while losing money" phase, before they inevitably charge a lot more and mess with the probability models to show secret ads, which will make customers hate them

I have sometimes wondered to myself if it would just be easier to make graphs of means with 83.5% CIs instead of 95% CIs just so it would match people's natural misconceptions

I mean yeah, but on the other hand they'd probably just end up making a million bucks by complaining loudly that they are being censored in the current media landscape, so I'd really just prefer that they become increasingly ignored.

#stats haiku sent to me by a student, from Angrist and Pischke, 2009, p. 8:

T-stats looks too good
Try cluster standard errors --
significance gone

Yeah, I'd seen this and I think it'll be a helpful tool as a prescreen for fake citations in many cases.

The think that initially surprised me about falsified results in Copilot is that most of the test statistics would pass internal consistency checks like this and statcheck.

Ohhh, inevitable but I still hate it

Yeah, I think the save is harder the more tadpoles you slurped up, so I guess the cost of ultimate power is being uggo

Truly awful Faustian deal, I know

Genius

Hmm, I'm open-minded so I got on the list to try it out, but it is a little "The only way to stop a bad guy with AI is a good guy with AI"

i'm sorry my review is late
i meant to read it weeks ago
but the writing
was actively hostile to comprehension
each paragraph
a slow erosion
of my will to live