Dan McNeish

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...

Pseudo Effects: How Method Biases Can Produce Spurious Findings About Close Relationships - Samantha Joel, John K. Sakaluk, James J. Kim, Devinder Khera, Helena Yuchen Qin, Sarah C. E. Stanton, 2025 Research on interpersonal relationships frequently relies on accurate self-reporting across various relationship facets (e.g., conflict, trust, appreciation). Y...

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/...

Assistant Professor, Psychological Sciences - Flagstaff, Arizona, United States About the Department/College The Department of Psychological Sciences is located on the Flagstaff Mountain Campus of Northern Arizona University (NAU), situated at the base of San Francisco Peaks. NAU...

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...

<em>Child Development</em> | SRCD Journal | Wiley Online Library Adverse childhood experiences (ACEs) are highly impactful stressors that increase individuals' risk for a plethora of negative developmental and health outcomes. Furthermore, minoritized groups and u...

‼️ 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

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(PDF) Missing Not at Random Intensive Longitudinal Data With Dynamic Structural Equation Models PDF | Intensive longitudinal designs are increasingly popular for assessing moment-to-moment changes in mood, affect, and interpersonal or health... | Find, read and cite all the research you need on ...

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!

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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).

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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

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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-...

Practical Implications of Sum Scores Being Psychometrics’ Greatest Accomplishment - Psychometrika This paper reflects on some practical implications of the excellent treatment of sum scoring and classical test theory (CTT) by Sijtsma et al. (Psychometrika 89(1):84–117, 2024). I have no major disag...

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

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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.

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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)

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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)

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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...

Dynamic Structural Equation Modeling Seminar | Statistics Course This online course by Dan McNeish Ph.D., introduces both foundational and intermediate topics in DSEM.

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!)

Missing Not at Random Intensive Longitudinal Data with Dynamic Structural Equation Models Hosted on the Open Science Framework

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 👇

Also, the MI definition in your 2022 preprint was really eye-opening (first found it through @dingdingpeng.the100.ci 's blog). It never struck me that MI could be conceived like that, super clever idea and love when different frameworks help see old ideas in new ways!

Good point, it would have been nice to see more on the causal perspectives but I thought the history part was fascinating since I have never seen it succinctly presented in one place.