Nate Phillips

Love to see a registered report that reinforces why the registered report is such a valuable tool

Interactions are difficult to detect in field studies as they are typically tiny--very small to start with and made smaller by the joint unreliabilities of the components. Here, we find some but the contribution to explained variance is negligible. Call off the search. It is not worth the effort.

Pretty excited about this one. In this paper, we discuss the replication/credibility crisis, the factors that contribute to it, and clinical psychology's slow (really slow) progress in dealing with it. We offer a competency-based fraemwork for improving our training of future scholars.
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Rigorous science is transparent science.

New paper led by @drlynam.bsky.social on the need for more training in and engagement with open science practices in clinical psych programs. It has been difficult to make progress due to a variety of barriers, including students working in labs uninterested or hostile to these approaches.

Just accepted from @vizecolin.bsky.social and myself. We coded Open Science practices (preregistration, RRs, open data, and open code) from 2021 to 2024 in two personality disorder journals (JPD, PDTRT) and three personality journals *JOP JRP, and EJP).
osf.io/preprints/ps...
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Lol 100 percent. I feel similarly when diving into this literature and posted this a little while ago hoping for some clarity (and got crickets)

bsky.app/profile/nphi...

A review of spline function procedures in R - BMC Medical Research Methodology Background With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. An important issue in splin...

Not sure if this is the type of work you're looking for, but I read this one recently and found it helpful! Interested to see what other folks recommend bmcmedresmethodol.biomedcentral.com/articles/10....

Additive and Interactive Relations of Personality and Cognition With Externalizing Behaviors - Nathaniel L. Phillips, Nathan T. Carter, Kevin M. King, Courtland S. Hyatt, Max M. Owens, Donald R. Lynam... Personality and cognition offer robust frameworks to understand the individual differences associated with externalizing behaviors. However, these literatures h...

𝐀𝐝𝐝𝐢𝐭𝐢𝐯𝐞 𝐚𝐧𝐝 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐨𝐟 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐖𝐢𝐭𝐡𝐄𝐱𝐭𝐞𝐫𝐧𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐬 | "Although interaction effects were detected, they were small and practically negligible in their explanation of variance in externalizing behaviors" journals.sagepub.com/doi/10.1177/...

This imagery is the kind of thing you see in video games to let you know you’re in a totalitarian city or country.

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...

This one and McCabe et al 2022 from @kevinmking.bsky.social's group are both awesome in thinking about the interpretability of these types of effects

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...

Really appreciate the authors’ efforts to differentiate these practices. I’ve definitely seen the term “preregistration” used to describe each of these three (registration, protocol, analysis plan) in isolation of one another, so it’s great to have a framework to address these jingle-jangle issues

We don't really think one will be able to cleanly divide the personality disorders from Axis I disorders. We argue (following Lilienfeld's writing on psychopathology's distinction from normality) that it is a Roschian construct such that there won't be an easy way to cleave PDs from other disorders.

Interpersonal and personality disorders: Commentary on Wright et al. (2022) - PubMed Wright et al. (2022) propose to replace personality disorders with a new classification of interpersonal disorders. We suggest that the trait model addresses well the limitations of the personality di...

For another perspective...

pubmed.ncbi.nlm.nih.gov/37523287/

There Is an Information Blackout at Florida’s ‘Alligator Alcatraz’ Migrant Detention Camp Public records related to Florida’s so-called “Alligator Alcatraz” migrant detention camp have...

Over 90 pages of "Alligator Alcatraz" files disappeared as I was looking at them and reporting on the situation. Our public records requests are getting stonewalled. A slew of experts told me this all seems to be illegal. talkingpointsmemo.com/muckraker/th...

Yeah, that checking the box was my intuition, too. Not surprising that these types of cases are pretty fringe though

Thanks for the response! Out of curiosity, how are y’all coding it if the raw data is coupled with open code showing the pipeline from raw data to clean data (as described in the manuscript)?

Let us know what you think!

Question for my open science peeps! @nphillips36.bsky.social and I are working on a lit review where we’re coding whether or not manuscripts have open data.
How should we handle cases where authors provide links to big, “open” datasets? In some of these cases, the data are hidden behind so much…

Reposting for the morning crowd — recs would be appreciated!

For the quanty folks: Any go-to readings comparing approaches to modeling nonlinearity? Seeing more on splines, fractional polynomials, etc., but struggling to find clear head-to-head comparisons or discussions of tradeoffs.

Final vote. 50-50. VP breaks the tie.

One single GOP Senator could have stopped this abomination. Saved millions of parents from watching their child go hungry. Saved the lives destroyed when Medicaid disappears.

They will all live forever with the horror of this bill.

Same here lol -- I did not post it separately on purpose

psycnet.apa.org/PsycARTICLES...

It's a good time to be an antagonism researcher (because of this issue and for the many other reasons)

Would love to see one of these reasonable applications, if you have any examples in mind. Thanks!

@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)

Very cool! I appreciate the thoughtful response and congrats on a really interesting paper