Todd Jennings

I have a full course on R for Psychology and Neuroscience, which includes sample programs and lecture videos. Also has some recommended books on R programming. Perfect for beginners!
rworkshop.missouri.edu

Would love some recommendations for biostats texts! Especially biostatistics using R. Feeling a little out of my depth with this, but hoping to find a great resource for self-teaching.

YouTube video by HiTOP Using HiTOP in Research

We’ve just published another video on HiTOP that tries to flesh out a bit more how the framework can be helpful in research: youtu.be/q0jOi_Nl1yo

We hope it’s useful, and are keen to hear any feedback

Thanks again so much to @tashtc.bsky.social for all of her hard work creating this video series ✨

🚨 Position Alert! 🚨
I am looking for a full-time Research Scientist 1 to join my lab in Albuquerque, NM at the Center on Alcohol, Substance Use, and Addiction (CASAA). We will be studying how stress and trauma influence cannabis and substance use. 🧠🌿 (1/5)

Development of an Offline-Friend Addiction Questionnaire (O-FAQ): Are most people really social addicts? - Behavior Research Methods A growing number of self-report measures aim to define interactions with social media in a pathological behavior framework, often using terminology focused on identifying those who are ‘addicted’ to engaging with others online. Specifically, measures of ‘social media addiction’ focus on motivations for online social information seeking, which could relate to motivations for offline social information seeking. However, it could be the case that these same measures could reveal a pattern of friend addiction in general. This study develops the Offline-Friend Addiction Questionnaire (O-FAQ) by re-wording items from highly cited pathological social media use scales to reflect “spending time with friends”. Our methodology for validation follows the current literature precedent in the development of social media ‘addiction’ scales. The O-FAQ had a three-factor solution in an exploratory sample of N = 807 and these factors were stable in a 4-week retest (r = .72 to .86) and was validated against personality traits, and risk-taking behavior, in conceptually plausible directions. Using the same polythetic classification techniques as pathological social media use studies, we were able to classify 69% of our sample as addicted to spending time with their friends. The discussion of our satirical research is a critical reflection on the role of measurement and human sociality in social media research. We question the extent to which connecting with others can be considered an ‘addiction’ and discuss issues concerning the validation of new ‘addiction’ measures without relevant medical constructs. Readers should approach our measure with a level of skepticism that should be afforded to current social media addiction measures.

Had missed this absolutely brilliant paper. They take a widely used social media addiction scale & replace 'social media' with 'friends'. The resulting scale has great psychometric properties & 69% of people have friend addictions.

link.springer.com/article/10.3...

Transformation starts at the periphery of networks where pushback is less - Scientific Reports Scientific Reports - Transformation starts at the periphery of networks where pushback is less

Intervening on a central node in a network likely does little given that its connected neighbors will "flip it back" immediately. Happy to see this position supported now.

"Change is most likely [..] if it spreads first among relatively poorly connected nodes."

www.nature.com/articles/s41...

“Not only is change from baseline a very problematic response variable; the very notion of patient improvement as an outcome measure can even be misleading. A patient who starts at the best level and who does not worsen should be considered a success.”

Tell me why: An ecological momentary assessment study of “unknown” substance use motive endorsement and the predictive utility of affect Theoretical models of substance use motives emphasize the role of affect when making decisions about use and propose that there are moments during whi…

So excited to share my first, first-author publication where we used EMA to examine the prospective relationship between momentary positive and negative affect and an “I don’t know” response option when asked about motives for use. www.sciencedirect.com/science/arti...

@drandreahoward.bsky.social, hold my beer..

Latent class growth models are worse than useless, and we've known this for more than 20 years.

(See Bauer 2007)

oh noooo

So much for the exclusion restriction I guess 😘

Chiles vs Salazar—Conversion Efforts Bans and Free Speech This Viewpoint discusses an upcoming US Supreme Court case that will examine whether Colorado’s ban on conversion efforts for minors appropriately regulates substandard clinical practice or is a viewp...

Our latest paper is out in JAMA Pediatrics, discussing the upcoming Supreme Court case that aims to overturn conversion therapy bans.
jamanetwork.com/journals/jam...

The psych job market may not be dead... but it is gravely injured 😬 So far it's looking like the Trump administration's attacks on higher ed/research are going to have more than 2x the impact on the job market as the covid-19 pandemic. #psychjobs #neurojobs #academicjobs

[cite_start]A screenshot of the first page of a court document from the United States District Court for the District of Massachusetts[cite: 3]. [cite_start]The document is titled "MEMORANDUM AND ORDER" [cite: 43] [cite_start]and was filed on September 3, 2025[cite: 1]. The document lists two related civil actions: * [cite_start]**Civil Action No. 25-cv-11048-ADB**: President and Fellows of Harvard College, et al., Plaintiffs, v. United States Department of Health and Human Services, et al., Defendants[cite: 4, 5, 14, 17, 21]. * [cite_start]**Civil Action No. 25-cv-10910-ADB**: American Association of University Professors - Harvard Chapter, et al., Plaintiffs, v. United States Department of Justice, et al., Defendants[cite: 22, 23, 24, 26, 27, 36]. [cite_start]The order is from Judge Burroughs, D.J.[cite: 44]. [cite_start]The text begins by identifying the plaintiffs in the consolidated cases[cite: 45].

A screenshot of pages 81 and 82 from a court order, detailing the judge's rulings and the relief granted. **Rulings:** * **For Harvard's case:** The court grants Harvard's motion for summary judgment on several counts, including violations of the First Amendment and Title VI. It grants in part Harvard's motion regarding the "Freeze Orders" and grants in part the defendants' motion regarding the "Termination Letters," citing a lack of jurisdiction. * **For the Organizational Plaintiffs' case:** The court grants the plaintiffs' motion for summary judgment on their First Amendment claims and denies the defendants' cross-motion on several counts. The ruling on the "Freeze Orders" and "Termination Letters" mirrors the one in Harvard's case. **Ordered Relief:** 1. The court vacates and sets aside the Freeze Orders as arbitrary and capricious. 2. The court vacates and sets aside the Freeze Orders and Termination Letters as violations of the First Amendment. 3. The court vacates and sets aside the Termination Letters as violations of Title VI. 4. The court permanently enjoins the defendants from implementing the Freeze Orders, Termination Letters, and their unconstitutional conditions.

JUST IN: Harvard won summary judgment against the Trump administration with the court finding that the admin violated Harvard's 1st amend. rights. The court has vacated the funding freeze orders and termination letters and issued a permanent injunction.

storage.courtlistener.com/recap/gov.us...

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

Preregistration does not improve the transparent evaluation of severity in Popper’s philosophy of science or when deviations are allowed - Synthese One justification for preregistering research hypotheses, methods, and analyses is that it improves the transparent evaluation of the severity of hypothesis tests. In this article, I consider two…

New paper by Mark Rubin confirms that my conceptual approach to justify the practice of preregistration is coherent, and a solid and strong logic that provides a basis for preregistrstion in science: link.springer.com/article/10.1...

I often talk to international colleagues about how understanding mass shootings in the US is misguided without understanding the larger context of the fact that we just tolerate excess death from **all causes** at a rate that most high income countries do not

SUPREME COURT OF THE UNITED STATES No. 25A103 NATIONAL INSTITUTES OF HEALTH, ET AL. U. AMERICAN PUBLIC HEALTH ASSOCIATION, ET AL. ON APPLICATION FOR STAY August 21, 2025] The application for stay presented to JUSTICE JACKSON and by her referred to the Court is granted in part and denied in part. The application is granted as to the District Court's judgments vacating the Government's termination of various research-related grants. See Department of Ed. v. California, 604 U.S. _ (2025) (per curiam). The Administrative Procedure Act's "limited waiver of [sovereign] immunity" does not provide the District Court with jurisdiction to adjudicate claims "based on" the research-related grants or to order relief designed to enforce any "obligation to pay money pursuant to those grants. Id., at _ (slip op., at 2). And while the loss of money is not typically considered irreparable harm, that changes if the funds "cannot be recouped" and are thus "irrevocably expended." Philip Morris USA Inc. v. Scott, 561 U. S. 1301, 1304 (2010) (Scalia, J., in chambers). The Government faces such harm here. The plaintiffs do not state that they will repay grant money if the Government ultimately prevails. Moreover, the plaintiffs' contention that they lack the resources to continue their research projects without federal funding is inconsistent with the proposition that they have the resources to make the Government whole for money already spent. The application is otherwise denied.

BREAKING: The Supreme Court, on a 5-4 vote with Justice Barrett as the deciding vote, allows the Trump administration to cancel NIH grants as part of the administration’s attack on “DEI” while litigation proceeds. Five justices write. www.supremecourt.gov/opinions/24p...

I will be interviewing for a clinical psychology PhD student in the Precision Psychopathology + Dynamic Immunopsychiatry Lab this interview cycle.

Please see our website for more info about what we do + share with applicants you think might be a good fit.

share.google/uJRyS3NY9Kdo...

⚠️ UNM has a new non-clinical PhD program, Diversity & Health Data Science Across the Lifespan! This area adopts a multidisciplinary lens to frame research questions at the intersection of human development, health, and sociocultural diversity.

Details and faculty here: psych.unm.edu/graduate/pro...

For the love of god put the sample size in the abstract

The fact that Paul says that Pearl is not afraid of "dumbing things down" has decreased my assessment of my own intelligence by a couple of points.

Today our @unm.edu @casaa.bsky.social T32 training grant is no longer funded. We are waiting for our renewal that is pending. I spent most of yesterday crying with worry about how I will support our amazing trainees. Today I celebrate them and the joy of seeing them present at CPDD and RSA. 🧵

The U.S. Senate is currently considering a budget reconciliation bill that quietly includes language that could devastate our public lands. It would:
- Make over 250 million acres of public land eligible for sale
- Mandate the sale of at least 2 million acres of Forest Service and BLM land

To proceed to the survey, please check off the box and click the button below.

Alright everyone, one last push.

If you or anyone you know is:
- LGBTQ+
- 13-24 years old
- In the US

Trevor Project is recruiting responses for their survey on LGBTQ+ mental health.

It has HUGE impacts on policy, please take it and share widely if you can :)

trvr.org/survey2025

Over 4.8 million and counting and many events haven’t even kicked off yet. We’re aiming for over 10 million today to hit the 3.5% rule. The crowds are massive and still growing. We are so proud of all of you. Huge shoutout to Texas you showed up!

They Asked an A.I. Chatbot Questions. The Answers Sent Them Spiraling.

This is the third story I've read in a month about how AI chatbots are leading people into psychological crises.

Gift link

medium.com/@malte.elson...
it was @malte.the100.ci blogging, but not at the 100% CI. we shouldnt speak of this dark period, but the advice is still useful.

Pro tip, folks: if you make a figure for a publication, don’t just hand the copyright over to the publisher. Post it on OSF first and then give yourself permission to use it in the first publication and then all future publications. That way, no permissions issues.

NIH indirect cost cuts will affect the economy and employment - Nature Human Behaviour Nature Human Behaviour - NIH indirect cost cuts will affect the economy and employment

www.nature.com/articles/s41...
Every $1 spent on NIH funding returns nearly $3 to the economy - an almost 200% return on investment💰.
Also 99% of drugs developed 2010-2019 were funded by NIH ‼️🤯
Cutting NIH funding not only hurts the economy but will hurt families and patients 😔💔