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Dan McNeish

@dmcneish.bsky.social
1.7K followers 225 following 60 posts

Quant Psyc professor at Arizona State. Into clustered data, latent variables, psychometrics, intensive longitudinal data, and growth modeling. sites.google.com/site/danielmmcneish

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Reposted by Dan McNeish
Laura M Stapleton @lauramstapleton.bsky.social · 28/06/2026
Quantitative Methodologists out there… we are hiring at the Assistant Professor level! Please consider joining our community! umd.wd1.myworkdayjobs.com/en-US/UMCP/j...
umd.wd1.myworkdayjobs.com
Assistant Professor
Job Description Summary Organization's Summary Statement: The Quantitative Methodology: Measurement and Statistics (QMMS) program in the Department of Human Development and Quantitative Methodology (H...
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Dan McNeish @dmcneish.bsky.social · 28/05/2026
It might be worth looking at Bauer’s work on trifactor models or integrative data analysis. Those models were designed to extract a single cohesive construct from different informants at different time points, so you might be able to treat different questionnaires as an “informant” in those models.
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Dan McNeish @dmcneish.bsky.social · 28/04/2026
Thanks for sharing, hope that it is useful for you and the lab!
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Dan McNeish @dmcneish.bsky.social · 28/04/2026
Glad to hear that the mind reading device I left at the SMEP meeting in New Mexico is still functioning properly 😈
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Dan McNeish @dmcneish.bsky.social · 23/04/2026
Link to the non-paywalled version: www.researchgate.net/publication/...
researchgate.net
(PDF) A Primer on Intensive Longitudinal Psychometrics
PDF | Many intensive longitudinal studies are interested in topics that are not always amenable to direct physical measurement and instead are often... | Find, read and cite all the research you need ...
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Dan McNeish @dmcneish.bsky.social · 23/04/2026
New paper— everyone is collecting intensive longitudinal data but recent review studies report that less than half of studies consider measurement/psychometrics. The paper covers a few foundational psychometric methods for ILD and provides a shiny app to apply them link.springer.com/article/10.3...
link.springer.com
A primer on intensive longitudinal psychometrics - Behavior Research Methods
Many intensive longitudinal studies are interested in topics that are not always amenable to direct physical measurement and instead are often theorized as latent constructs (e.g., affect, emotion, mo...
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Yi Feng @yifeng.bsky.social · 27/02/2026
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/
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Annual Reviews @annualreviews.bsky.social · 21/01/2026
📢 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
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Björn Siepe @bsiepe.bsky.social · 22/10/2025
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...
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Samantha Joel @datingdecisions.bsky.social · 10/09/2025
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/...
journals.sagepub.com
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...
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Dan McNeish @dmcneish.bsky.social · 03/09/2025
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...
careers.nau.edu
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...
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Reposted by Dan McNeish
Quantitude the Podcast @quantitude.bsky.social · 19/08/2025
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...
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John Sakaluk @johnsakaluk.bsky.social · 10/07/2025
🧵 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...
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Reposted by Dan McNeish
Daniel P. Moriarity @dpmoriarity.bsky.social · 22/04/2025
‼️ 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
srcd.onlinelibrary.wiley.com
<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...
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Dan McNeish @dmcneish.bsky.social · 02/04/2025
Also, here's a non-paywalled link www.researchgate.net/publication/...
researchgate.net
(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 ...
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Dan McNeish @dmcneish.bsky.social · 02/04/2025
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
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Dan McNeish @dmcneish.bsky.social · 02/04/2025
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
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Dan McNeish @dmcneish.bsky.social · 02/04/2025
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
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Dan McNeish @dmcneish.bsky.social · 02/04/2025
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-...
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Dan McNeish @dmcneish.bsky.social · 21/02/2025
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...
link.springer.com
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...
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Dan McNeish @dmcneish.bsky.social · 18/02/2025
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
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Dan McNeish @dmcneish.bsky.social · 18/02/2025
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
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Dan McNeish @dmcneish.bsky.social · 18/02/2025
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
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Dan McNeish @dmcneish.bsky.social · 18/02/2025
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
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Dan McNeish @dmcneish.bsky.social · 18/02/2025
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
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Dan McNeish @dmcneish.bsky.social · 18/02/2025
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
dynamicfit.app
Reliability Representativeness
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Dan McNeish @dmcneish.bsky.social · 18/02/2025
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...
link.springer.com
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Dan McNeish @dmcneish.bsky.social · 29/01/2025
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...
osf.io
OSF
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Dan McNeish @dmcneish.bsky.social · 29/01/2025
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...
statisticalhorizons.com
Dynamic Structural Equation Modeling Seminar | Statistics Course
This online course by Dan McNeish Ph.D., introduces both foundational and intermediate topics in DSEM.
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Dan McNeish @dmcneish.bsky.social · 17/01/2025
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!)
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Dan McNeish @dmcneish.bsky.social · 15/01/2025
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
osf.io
Missing Not at Random Intensive Longitudinal Data with Dynamic Structural Equation Models
Hosted on the Open Science Framework
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Dan McNeish @dmcneish.bsky.social · 05/12/2024
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.
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Kevin M. King @kevinmking.bsky.social · 30/11/2024
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?
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Kevin M. King @kevinmking.bsky.social · 23/11/2024
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.
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Reposted by Dan McNeish
Markus Eichhorn @markuseichhorn.bsky.social · 22/11/2024
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 👇
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Dan McNeish @dmcneish.bsky.social · 15/11/2024
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!
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Dan McNeish @dmcneish.bsky.social · 15/11/2024
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.
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Dan McNeish @dmcneish.bsky.social · 13/11/2024
This article gives a phenomenal overview of the history and evolution of best practices in measurement invariance and differential item functioning -- I can't remember the last time I learned so much reading a single paper, highly recommended! www.tandfonline.com/doi/full/10....
tandfonline.com
A Review of Some of the History of Factorial Invariance and Differential Item Functioning
The concept of factorial invariance has evolved since it originated in the 1930s as a criterion for the usefulness of the multiple factor model; it has become a form of analysis supporting the vali...
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Dan McNeish @dmcneish.bsky.social · 05/11/2024
Posting for a Bsky-less friend -- New quant job posting for an Asst Professor at the University of Georgia in machine learning/data science/AI (very broadly defined) with applications to educational research. Due date is Dec 2! www.ugajobsearch.com/postings/403...
ugajobsearch.com
Assistant Professor (Quantitative Methodology)
The Quantitative Methodology (QM) Program in Department of Educational Psychology at the University of Georgia invites applications for a tenure-track Assistant Professor with expertise in Educational...
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Dan McNeish @dmcneish.bsky.social · 31/10/2024
Non-paywalled links, www.researchgate.net/publication/... OR osf.io/preprints/ps... /6
researchgate.net
(PDF) Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models
PDF | On Mar 12, 2024, Daniel McNeish and others published Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models | Find, read and cite all the research you need on Res...
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Dan McNeish @dmcneish.bsky.social · 31/10/2024
New limitation is that magnitude of discrepancy added to the model-implied correlation matrix is moderately correlated with derived cutoffs, so DDDFI is more circular than original DFI Next step is to try to figure out matrix perturbation method that is less correlated fit indices /5
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Dan McNeish @dmcneish.bsky.social · 31/10/2024
In addition to extending the types of models that can be supported, DDDFI also (a) gives identical cutoffs for equivalent models, (b) standardizes comparisons for models with a different factor structure/number of factors, and (c) also can incorporate the exact missing data pattern into cutoffs /4
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Dan McNeish @dmcneish.bsky.social · 31/10/2024
Extension required moving away from identifying specific hypothetical paths to omit in fit index simulations. Instead, misspecification is added directly to the model-correlation matrix /3
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Dan McNeish @dmcneish.bsky.social · 31/10/2024
Shiny app (www.dynamicfit.app) is updated with new integrative app that can do original DFI or the updated DDDFI method (more detailed post on the updated shiny app coming soon) CRAN version of the package is not yet updated. /2
dynamicfit.app
Posit Connect
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Dan McNeish @dmcneish.bsky.social · 31/10/2024
New Paper w/@melissagwolf.bsky.social- Dynamic fit cutoffs now support any covariance structure model (e.g., bifactor, hierarchical, & mediation models). Also supports categorical and non-normal outcomes Use the DDDFI function in dynamic package on GitHub www.tandfonline.com/doi/full/10.... /1
tandfonline.com
Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models
Despite the popularity of traditional fit index cutoffs like RMSEA ≤ .06 and CFI ≥ .95, several studies have noted issues with overgeneralizing traditional cutoffs. Computational methods have been ...
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Dan McNeish @dmcneish.bsky.social · 25/10/2024
Might be out of scope, but my non-economist collaborators sometimes had trouble with econometric papers so I tried to translate the ideas of fixed effect models/clustered errors and how they relate the multilevel models for a psych/education audience, psycnet.apa.org/record/2024-...
psycnet.apa.org
APA PsycNet
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Dan McNeish @dmcneish.bsky.social · 25/09/2024
No surprised he brought it up long before we did! It's definitely uncomfortable to look at the models and see that they have different meanings and then write the model equations out to see that they are the same. It seems like there should be a simple fix, but it gets really messy really fast!
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Dan McNeish @dmcneish.bsky.social · 25/09/2024
Similar idea as the SAM work! ( p. 13 of the journal version talks about relation between the two methods). The difference is the mechanism. I believe the SAM work relies on a correction, our paper tries to use Bayes to model things directly. There are pros and cons of each approach, of course!
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Dan McNeish @dmcneish.bsky.social · 25/09/2024
Non-paywalled version: www.researchgate.net/publication/...
researchgate.net
(PDF) Measurement and Uncertainty Preserving Parametric Modeling for Continuous Latent Variables With Discrete Indicators and External Variables
PDF | Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related... | Find, read and cite all the research you ne...
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