Brady West
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bradytwest.bsky.social
Brady West
@bradytwest.bsky.social

Research Professor @ Univ. of MI Institute for Social Research (ISR) and Department of Biostatistics. Usually discussing statistics, surveys, sports, music, and leadership. Personal website: www.umich.edu/~bwest

Brady Thomas West is an American statistician, academic and author. He is a research professor in the Survey Methodology Program (SMP) at the Survey Research Center (SRC) in the Institute for Social Research (ISR), and a research professor in the Department of Biostatistics within the School of Public Health, both at the University of Michigan, Ann Arbor. He also serves as an Adjunct Research Professor in the Joint Program in Survey Methodology (JPSM) at the University of Maryland, College Park and as an Adjunct Instructor at the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. .. more

Sociology 18%
Mathematics 18%

This was an absolute all-timer of a Gus Johnson call, which is appropriate given how utterly incredible the catch was. Crazy!
GUS-SPLOSIUON

Reposted by Brady T. West

GUS-SPLOSIUON
LLMs are now widely used in social science as stand-ins for humans—assuming they can produce realistic, human-like text

But... can they? We don’t actually know.

In our new study, we develop a Computational Turing Test.

And our findings are striking:
LLMs may be far less human-like than we think.🧵
Computational Turing Test Reveals Systematic Differences Between Human and AI Language
Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior, based on the assumption that they can generate realistic, human-like text. Yet this assumption rem...
arxiv.org

Reposted by Brady T. West

Struggling with missing data? Join Paul Allison for "Missing Data Using R", a 4-week on-demand seminar starting Nov. 17. You'll learn maximum likelihood & multiple imputation—two of the most powerful #missingdata solutions using #Rstats.
Missing Data Using R | On-Demand Seminar | Statistical Horizons
Paul Allison, Ph.D., teaches this self-paced course on missing data using R, covering multiple imputation and maximum likelihood for accurate, unbiased results.
statisticalhorizons.com

Reposted by Brady T. West

Data scientists perform last rites for ‘dearly departed datasets’ in 2nd Trump administration

Thank you @mikeysid.bsky.social for covering the loss of public data.

apnews.com/article/cens...
Data scientists perform last rites for 'dearly departed datasets' in 2nd Trump administration
A group of U.S. data scientists has published a list of federal datasets that have been altered or removed since President Donald Trump returned to the White House.
apnews.com

Reposted by Brady T. West

New blog post: open-source software packages have surprising problems with the way they calculate weighted medians and other quantiles.

www.practicalsignificance.com/posts/weight...

#rstats #julialang
Weighted Quantile Weirdness and Bugs – Practical Significance
Computing quantiles is surprisingly complicated. It gets much weirder when you use weights, and popular software behaves in surprising ways that might trouble you.
www.practicalsignificance.com

Sooo Michigan Hoops might be as good as advertised this year? 😳

Reposted by Brady T. West

@tschepis.bsky.social, Philip Veliz, @bradytwest.bsky.social, Jason A. Ford, and Sean Esteban McCabe used national survey data to identify motive classes for nonmedical tranquilizer use among US teens & young adults and explored links to other substance use and mental health outcomes myumi.ch/XykGZ
Sage Journals: Discover world-class research
Subscription and open access journals from Sage, the world's leading independent academic publisher.
myumi.ch

Reposted by Brady T. West

This, by @vincentab.bsky.social, is the most straight-up *useful* book I’ve bought in a long while.

Reposted by Brady T. West

Roderick Little contributed Pattern-Mixture Models for Missing Data to the JAMA Guide to Statistics and Methods. In the essay, Little discusses the advantages and limitations of pattern-mixture models which allow the predictive distribution of missing variables myumi.ch/bVwZk
Pattern-Mixture Models for Missing Data
This JAMA Guide to Statistics and Methods article discusses designing and implementing clinical trials to minimize the number of missing data.
myumi.ch
Mark your calendar for next week's free, online half-day PDHP Workshop with @alussier.bsky.social: demonstrating the Structured Life Course Modelling Approach (SLCMA) applied to High Dimensional Data. Register at psc.isr.umich.edu/events/pdhp-...

Just very odd out of a bye week. Did they not see film showing that the Vikings blitz a lot? 🙄

This has been a very disappointing meltdown by all three phases for the Lions. Just a lot of bad football this weekend. 👎 But nice to see JJ have a solid game.

This is big if true…
Who did this?!

Reposted by Brady T. West

The target population for the research question is the population of people who would satisfy the eligibility criteria. “
Wrong x 2
1) The eligibility criteria define who doesn’t come into the trial not who does.
2) We generalise from quite different trials to populations eg bioequivalence trials.

I would encourage you to at least read the Executive Summary of this excellent report. 💯
🚨It's finally here!🚨
AAPOR's Taskforce on 2024 Pre-Election Polling report is out!

Full report: /https://aapor.org/wp-content/uploads/2025/10/AAPOR-Task-Force-on-2024-Pre-Election-Polling_Report.pdf

Executive summary: aapor.org/wp-content/u...
aapor.org
🚨It's finally here!🚨
AAPOR's Taskforce on 2024 Pre-Election Polling report is out!

Full report: /https://aapor.org/wp-content/uploads/2025/10/AAPOR-Task-Force-on-2024-Pre-Election-Polling_Report.pdf

Executive summary: aapor.org/wp-content/u...
aapor.org
Ever wondered how to get better survey feedback from respondents? 🧐

👉 Check out our new @jssam.bsky.social paper together with @jkhoehne.bsky.social @jessicakuhlm.bsky.social testing different (1) visual designs and (2) answer formats of #FinalCommentQuestions.

🌐 academic.oup.com/jssam/advanc...
Asking for Feedback: Innovating Final Comment Questions in Self-Administered Web Surveys
Abstract. Web surveys frequently include so-called “final comment questions” (FCQs) to provide respondents the opportunity to express their experiences wit
academic.oup.com
🎉 @rpsychologist.com 's PowerLMM.js is the online statistics application of the year 2025 🎉

powerlmmjs.rpsychologist.com

- Calculate power (etc) for multilevel models
- Examine effects of dropout and other important parameters
- Fast! (Instant results)

More important computational science that may not happen because it mentions diversity and inclusion. Fun times…and yes, definitely worth a donation if you are a Python user.
The Python Software Foundation got a competitive US research grant, but it came with a condition that they recant and abjure any diversity and inclusion ideas, on penalty of having to repay the money.

Obviously this is not desirable or safe, so no grant.

Donations would help them not regret this

Reposted by Brady T. West

📘 𝗡𝗲𝘄 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗼𝗳 𝙎𝙪𝙧𝙫𝙚𝙮 𝙍𝙚𝙨𝙚𝙖𝙧𝙘𝙝 𝙈𝙚𝙩𝙝𝙤𝙙𝙨:
“Survey Climate and Trust in Scientific Surveys.”

Explore how the relationship between surveys and society has evolved.

🔗 Read the 𝗳𝘂𝗹𝗹 𝗶𝘀𝘀𝘂𝗲 here:
ojs.ub.uni-konstanz.de/srm/issue/vi...

#SurveyResearch #SurveyMethods #SurveyClimate #ESRA
Vol. 19 No. 3 (2025): Survey Climate and Trust in Scientific Surveys | Survey Research Methods
ojs.ub.uni-konstanz.de

Reposted by Brady T. West

The Python Software Foundation got a competitive US research grant, but it came with a condition that they recant and abjure any diversity and inclusion ideas, on penalty of having to repay the money.

Obviously this is not desirable or safe, so no grant.

Donations would help them not regret this
The PSF has withdrawn $1.5 million proposal to US government grant program
In January 2025, the PSF submitted a proposal to the US government National Science Foundation under the Safety, Security, and Privacy of Open Source Ecosystems program to address structural vulnerabilities in Python and PyPI. It was the PSF’s first time applying for government funding, and navigating the intensive process was a steep learning curve for our small team to climb. Seth Larson, PSF Security Developer in Residence, serving as Principal Investigator (PI) with Loren Crary, PSF Deputy Executive Director, as co-PI, led the multi-round proposal writing process as well as the months-long vetting process. We invested our time and effort because we felt the PSF’s work is a strong fit for the program and that the benefit to the community if our proposal were accepted was considerable. We were honored when, after many months of work, our proposal was recommended for funding, particularly as only 36% of new NSF grant applicants are successful on their first attempt. We became concerned, however, when we were presented with the terms and conditions we would be required to agree to if we accepted the grant. These terms included affirming the statement that we “do not, and will not during the term of this financial assistance award, operate any programs that advance or promote DEI, or discriminatory equity ideology in violation of Federal anti-discrimination laws.” This restriction would apply not only to the security work directly funded by the grant, **but to any and all activity of the PSF as a whole**. Further, violation of this term gave the NSF the right to “claw back” previously approved and transferred funds. This would create a situation where money we’d already spent could be taken back, which would be an enormous, open-ended financial risk. Diversity, equity, and inclusion are core to the PSF’s values, as committed to in our mission statement: > _The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of**a diverse and international community** of Python programmers._ Given the value of the grant to the community and the PSF, we did our utmost to get clarity on the terms and to find a way to move forward in concert with our values. We consulted our NSF contacts and reviewed decisions made by other organizations in similar circumstances, particularly The Carpentries. In the end, however, the PSF simply can’t agree to a statement that we won’t operate any programs that “advance or promote” diversity, equity, and inclusion, as it would be a betrayal of our mission and our community. We’re disappointed to have been put in the position where we had to make this decision, because we believe our proposed project would offer invaluable advances to the Python and greater open source community, protecting millions of PyPI users from attempted supply-chain attacks. The proposed project would create new tools for automated proactive review of all packages uploaded to PyPI, rather than the current process of reactive-only review. These novel tools would rely on capability analysis, designed based on a dataset of known malware. Beyond just protecting PyPI users, the outputs of this work could be transferable for all open source software package registries, such as NPM and Crates.io, improving security across multiple open source ecosystems. In addition to the security benefits, the grant funds would have made a big difference to the PSF’s budget. The PSF is a relatively small organization, operating with an annual budget of around $5 million per year, with a staff of just 14. $1.5 million over two years would have been quite a lot of money for us, and easily the largest grant we’d ever received. Ultimately, however, the value of the work and the size of the grant were not more important than practicing our values and retaining the freedom to support every part of our community. The PSF Board voted unanimously to withdraw our application. Giving up the NSF grant opportunity—along with inflation, lower sponsorship, economic pressure in the tech sector, and global/local uncertainty and conflict—means the PSF needs financial support now more than ever. We are incredibly grateful for any help you can offer. If you're already a PSF member or regular donor, you have our deep appreciation, and we urge you to share your story about why you support the PSF. Your stories make all the difference in spreading awareness about the mission and work of the PSF. How to support the PSF: * Become a Member: When you sign up as a Supporting Member of the PSF, you become a part of the PSF. You’re eligible to vote in PSF elections, using your voice to guide our future direction, and you help us sustain what we do with your annual support. * Donate: Your donation makes it possible to continue our work supporting Python and its community, year after year. * Sponsor: If your company uses Python and isn’t yet a sponsor, send them our sponsorship page or reach out to sponsors@python.org today. The PSF is ever grateful for our sponsors, past and current, and we do everything we can to make their sponsorships beneficial and rewarding.
pyfound.blogspot.com

This game is setting football back a bit. 😂🤨

But Jimmy MFin Rolder came to drink milk and kick ass. And the milk is gone.

All Michigan had to do was execute and not make this a dogfight. Congrats, you now have a dogfight. 🙄 Get your shxt together.

A reminder that I will be giving this webinar today as part of ICPSR’s #SBECCC. This is the first time that I’ll be introducing this work, which we believe has great potential to improve the quality of imputations in a variety of settings. Would love to hear thoughts! Link below.
Save the date for an upcoming #sbeccc webinar featuring @bradytwest.bsky.social on October 23rd! Learn more: myumi.ch/E8Ap6

This Lions defense is redefining what “next man up” means tonight. What an effort! #FDTF

Reposted by Brady T. West

Reposted by Brady T. West

Micha Fischer, Roderick Little & @bradytwest.bsky.social investigate multiple imputation under missing not at random assumptions w/response indicators as covariates, finding in MNAR data scenarios, methods including RIs can improve performance for both analytic & descriptive inference myumi.ch/n19nk
Multiple imputation under missing not at random: incorporating response indicators into sequential imputation
Multiple imputation (MI) of missing values is mostly applied under the assumption of missing at random (MAR), but the alternative missing not at random (MNAR) assumption may be more plausible. MI a...
myumi.ch

Yes. And this is 100% on the coaching staff. Game 7. Play your best players. Otherwise you are going to lose what by all accounts is a pretty big fan base.
Michigan is constantly playing defensive players who are not as good as their starters for no reason whatsoever