A few weeks ago you’ve heard about nmax as the insurance policy for sample size planning on @quantitude.bsky.social
Today we’re excited to launch the nmax Shiny app. Sample size planning with nmax can now be done in just a few button clicks!
🚀 yifeng-quant.shinyapps.io/nmax/
📢 The most recent volume of the Annual Review of Psychology is now online! The most read article so far is "How Do Psychologists Determine Whether a Measurement Scale Is Good? A Quarter-Century of Scale Validation with Hu & Bentler (1999)" by @dmcneish.bsky.social.
arevie.ws/4jRYY6B
We built the openESM database:
▶️60 openly available experience sampling datasets (16K+ participants, 740K+ obs.) in one place
▶️Harmonized (meta-)data, fully open-source software
▶️Filter & search all data, simply download via R/Python
Find out more:
🌐 openesmdata.org
📝 doi.org/10.31234/osf...
In a new paper, my colleagues and I set out to demonstrate how method biases can create spurious findings in relationship science, by using a seemingly meaningless scale (e.g., "My relationship has very good Saturn") to predict relationship outcomes. journals.sagepub.com/doi/10.1177/...
Tenure-Track Quant Psyc job opening at Northern Arizona University in Flagstaff. Areas of interest are pretty broad (SEM, multilevel, or psychometrics), the deadline to apply is coming up soon (Sept 15) if you're interested!
careers.nau.edu/jobs/assista...
For those interested, here is a link to a new power paper:
Hancock, G. R., & Feng, Y. (2026). nmax and the quest to
restore caution, integrity, and practicality to the sample size planning process. Psychological Methods.
yifengquant.github.io/Publications...
🧵
Very excited (w/ @omarjcamanto.bsky.social) to share our preprint tutorial for using our R 📦 dySEM for #dyadic data analysis with latent variables, in cross-sectional data sets.
This paper has been literal years in the making, and provides three distinct tutorials.
osf.io/preprints/ps...
‼️ New paper @ Child Development discussing why it is inappropriate to use traditional common factor models to model adverse childhood experienced (ACEs) and other stressor inventories ‼️
Read full (brief) paper at srcd.onlinelibrary.wiley.com/doi/full/10....
@srcdorg.bsky.social #PsychSciSky
1/9
Also, here's a non-paywalled link
www.researchgate.net/publication/...
These kind of models make lots of assumptions, so make sure not to skip the limitations section if you're considering something like this!
/4
Trying the model out on the motivating empirical data and made a huge difference, changing the sign and conclusion about the intervention effect (2nd and 3rd row in the image, left vs. right column).
/3
The paper basically takes the Diggle-Kenward model from growth model in tries to jam it into a multilevel autoregressive model/DSEM.
Some simulations showed that it worked well, was much better than models that assume MAR when data are MNAR, and that it recovers true values pretty well
/2
New paper on dealing with MNAR intensive longitudinal data. Ran into this problem in an empirical study and didn't find too much in the methods literature on MNAR ILD, so this was the best I could come up with. Lots of opportunity to improve methods in this area!
psycnet.apa.org/record/2025-...
Recent papers from personality cited in the “Directly using items as predictors” section of the paper below basically argue that it doesn’t matter what items measure as long as they predict a relevant outcome (which sounds like predictive > other validity)
link.springer.com/article/10.1...
This work was part of a project funded by the US Dept of Education/IES, which has been a major supporter of pure methods/statistics/psychometrics work in US so that people like me don't have to beg substantive people to tack a methods aim onto an empirical grant
/end
Goal is hopefully to help researchers be a little more articulate about reporting reliability of scale scores and incorporate more recent ideas from the psychometric literature on conditional reliability when it may be appropriate and complement summary indices like alpha or omega
/6
The output also provides a number between 0 and 100.
Values close to 100 indicate that alpha/omega represent most scores well.
Values close to 0 indicate that scores have heterogeneous reliability and a summary does not describe some of the sample very well.
/5
Result is a plot that looks like this -- the conditional reliability at each score (the colored line; color indicates how many people are at that scores) is plotted against the alpha/omega summary index (black line)
/4
Shiny input looks like this -- upload the data, identify the scale items, the desired coefficient, and choose a method from which to calculate the "reliability representativeness" (different methods discussed in the paper)
/3
Basic idea borrows conditional reliability from IRT literature and compares the discrepancy of the conditional reliability function to a single summary like alpha/omega.
Shiny app to implement the method is located at dynamicfit.app/RelRep/
/2
New paper - coefficients like alpha/omega are commonly reported to summarize reliability. A sneaky nuance is that reliability is actually different at each score in the data. Paper tries to quantify how representative alpha/omega are of a typical score.
link.springer.com/article/10.3...
This paper on intensive longitudinal reliability led by Sebastian Castro-Alvarez is one of the best I've read in a while -- the review was so thorough, the code was fantastic, and it answered any questions I had about IL reliability. Definitely check it out you work with ILD!
osf.io/preprints/ps...
Next week, I'm teaching a 3-day workshop on DSEM for intensive longitudinal data using Mplus and registration is still open -- more information about the topics and registration can be found here!
statisticalhorizons.com/seminars/dyn...
Yes, I think you'd have use Bayesian methods in Mplus. I also don't think you could do a continuous time version in Mplus because I don't think that they've added support for binary variables in continuous time (although I might be behind on what is supported!)
The OSF link is here if you’re interested, osf.io/be8h3/
It’s intensive longitudinal data where where the outcome is a binary self-report question on binge eating. There’s 50% missingness and a suspected MNAR process where people don’t respond to the binge eating question when they binge eat
I made this switch a few years ago and the another thing that came up was that R (at least lme4 ) gives a lot more convergence warnings and errors than SAS, even when the output is identical. McCoach (2018, JEBS) studied this systematically and found similar results.
I don't understand how you can read and understand an evolving literature without keeping up with methodological developments.
What are we supposed to do, just read discussion sections and take people's word for it?
The basic principle is that reviewers are tired, they're rushed, they're reading your and 10 other grants on top of their normal workload.
Anything you can do to make it easy for them will help.
Here's a list of my suggestions.
Who should you nominate as reviewers for your manuscript? After a long chat with a post-grad it appears that there's a lot of misguided advice out there. Thread 👇
