Nate Phillips
@nphillips36.bsky.social
1.3K followers 760 following 110 posts
UGA Clinical Psych PhD student. Interested in personality, externalizing behaviors, open science, and methods.
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nphillips36.bsky.social
Love to see a registered report that reinforces why the registered report is such a valuable tool
gordonhodsonphd.bsky.social
#AcademicSky #PsychSciSky #MetaScience

New study in AMPPS:

Clinical psychologists favour statistically significant results.

(presumably true in all fields, I'd say. Bias against null findings)

journals.sagepub.com/doi/epub/10....
Reposted by Nate Phillips
drlynam.bsky.social
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.
davidbaranger.bsky.social
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/...
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...
journals.sagepub.com
Reposted by Nate Phillips
drlynam.bsky.social
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.
1/2
psyarxivbot.bsky.social
The Open Science Movement and Clinical Psychology Training: Rigorous Science is Transparent Science: https://osf.io/s46wd
Reposted by Nate Phillips
dsbarra.bsky.social
Rigorous science is transparent science.
jdmiller.bsky.social
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.
psyarxivbot.bsky.social
The Open Science Movement and Clinical Psychology Training: Rigorous Science is Transparent Science: https://osf.io/s46wd
Reposted by Nate Phillips
jdmiller.bsky.social
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.
psyarxivbot.bsky.social
The Open Science Movement and Clinical Psychology Training: Rigorous Science is Transparent Science: https://osf.io/s46wd
Reposted by Nate Phillips
drlynam.bsky.social
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...
1/5
OSF
osf.io
nphillips36.bsky.social
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...
nphillips36.bsky.social
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.
Reposted by Nate Phillips
davidbaranger.bsky.social
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/...
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...
journals.sagepub.com
Reposted by Nate Phillips
1in9.bsky.social
This imagery is the kind of thing you see in video games to let you know you’re in a totalitarian city or country.
jbendery.bsky.social
The irony/idiocy of this banner currently draped on the Department of Labor building is not lost on me, given all the federal workers he's forced out of their jobs.

(Pic via Getty Images)
Reposted by Nate Phillips
dingdingpeng.the100.ci
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...
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.
nphillips36.bsky.social
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
Reposted by Nate Phillips
quantitude.bsky.social
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...
nphillips36.bsky.social
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
katiecorker.bsky.social
"Preregistration" lumps three distinct practices into one:

- Study registration
- Protocol
- Analysis plans (SAPs)

To advance open science, it's critical that we distinguish them.

🌟 🌟 Now in-press paper from Evan Mayo-Wilson, @seangrant.bsky.social, David Moher, me: osf.io/preprints/me...
OSF
osf.io
Reposted by Nate Phillips
jdmiller.bsky.social
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.
Reposted by Nate Phillips
hunterw.bsky.social
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...
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...
talkingpointsmemo.com
nphillips36.bsky.social
Yeah, that checking the box was my intuition, too. Not surprising that these types of cases are pretty fringe though
nphillips36.bsky.social
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)?
nphillips36.bsky.social
Let us know what you think!
courtlandhyatt.bsky.social
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…
Reposted by Nate Phillips
courtlandhyatt.bsky.social
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…
nphillips36.bsky.social
Reposting for the morning crowd — recs would be appreciated!
nphillips36.bsky.social
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.
nphillips36.bsky.social
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.
Reposted by Nate Phillips
chrismurphyct.bsky.social
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.
nphillips36.bsky.social
Same here lol -- I did not post it separately on purpose