Two articles on the role of #MedicalStatistics in #HealthSciences which are a good #CPD read for both #QuantitativeMethods experts as well as health content experts:

Medical statisticians: Always on tap, but never on top?
https://eprints.whiterose.ac.uk/id/eprint/211342/

And this paper aims to provide guidance on why, when and on what to consult with statisticians when setting up a research project
https://www.cambridge.org/core/journals/bjpsych-advances/article/statistical-consulting-guidelines-for-new-researchers-in-psychiatry-and-mental-health-beyond-chatgpt/22BD35892A1760918B5BFBC0EBE5D7C4

#ECR #ProjectDesign #RCTs #Trials #Epidemiology

Medical statisticians: Always on tap but never on top? - White Rose Research Online

Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python

📘 Mastering Modern Time Series Forecasting (early access)The book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems. 💸 Pricing 🎉 Standard Edition Price: $40 | Minimum: $35Will increase to $80+ as content grows. A tremendous amount of work and expertise has gone into this book, which is designed to deliver exponential improvement to your forecasting skills, your company's bottom line and ROI, and your career. Forecasting is one of the most in-demand skills across nearly every industry today. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why This Book Stands Out🔑 Forecasting models are only 5% of the equation.The other 95%? It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.🧠 Built for understanding — not just coding.Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.💻 Clear, transparent, production-ready code.No obfuscation, no throwaway scripts. Every example is fully documented, reusable, and ready for real-world use.🔄 Continuously improved through real feedback.This is a living resource shaped by an active community of readers. Many improvements and additions come directly from their thoughtful feedback — and all readers get lifetime updates, including new chapters and bonus tools. Thank you to all contributors — your insights are recognized and appreciated in the book.📚 Comprehensive, real-world coverage.From classical time series models to deep learning and forecasting-specific transformers (FTSMs), the book covers a wide range — but always with a practical lens. Every method has been tested in production or validated against strong academic benchmarks. No fluff, just tools that work.📈 Real ROI — for your company and your career.Readers often see immediate improvements in model accuracy, interpretability, and stakeholder trust. No more silent failures or fragile production systems. This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. 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Gumroad

These sound interesting:

Carnegie Vacation #Scholarships aim to encourage undergraduates to develop their independent research skills. https://carnegie-trust.org/award-schemes/vacation-scholarships/

#Funding for students enrolled on an undergraduate degree course in #Scotland #Alba

Anyone interested? #QuantitativeMethods #Psychometrics

Vacation Scholarships - The Carnegie Trust for the Universities of Scotland

undergraduate research vacation scholarships research skills development scholarships research funding scottish universities summer project

The Carnegie Trust for the Universities of Scotland

My previous pinned #introductionpost post is quite old, so here's a new go ...

I teach #linguistics at the #UniversityOfAlberta. Professional interests include #AccessiblePedagogy, #Phonetics, #QuantitativeMethods (#statistics), #SpeechTechnology, #InternetLinguistics, #WritingSystems, and #EnglishSpellingReform

Outside of work, I like #ScottishCountryDancing, #Canoeing, and various nerdy pursuits such as #TTRPGs and #StarTrek. 🤓

How can we use #QuantitativeMethods to automatically classify medieval hagiographic manuscripts & analyse their internal structure, their origins and relations? Sébastien de Valeriola (ULB, Brussels) explores this question in this week's #DigitalHistoryOFK by examining manuscripts that exhibit a particular internal organisation: per circulum anni. Not to be missed!

Open to all!

🔜 Wed, 10 July, 4-6 pm, Zoom
ℹ️ https://dhistory.hypotheses.org/8104

#MedievalHistory #DigitalHistory @histodons

Sébastien de Valeriola: Mining legendaries: a quantitative approach to hagiographic manuscripts

Since the middle of the 17th century, scholars have been systematically describing numerous medieval manuscripts preserved in libraries and religious institutions that contain hagiographic texts, that is texts recounting the lives of saints. In this talk, we will show how one can apply quantitative tools to the resulting database to consider these codices from a … „Sébastien de Valeriola: Mining legendaries: a quantitative approach to hagiographic manuscripts“ weiterlesen

Digital History Berlin
📣Last few days! 📣If you haven't already, please complete our quick survey to inform upcoming #FemQuantSeminar & other #FemQuant events #feminist #quantitativemethods
https://forms.gle/gayFR3fh6ZJwMpeWA
Survey of FemQuant network

FemQuant was formed in 2017 following our first one-day conference focused on Feminist Approaches to Quantitative Social Science. Since then we have organised a number of events, including our monthly seminar series, and the organising collective has grown and become more international. The goal of our activities remains to bring together researchers who are using feminist theory and motivations in current quantitative, empirical research across the social sciences. We aim for the network to be a space and a community for researchers to discuss and debate the possibilities of a quantitative, feminist social science, and to learn from each other’s successes and difficulties in integrating feminist theory with quantitative methods. As an organising collective we would like to collect views from the wider network of what events and activities you find interesting and useful and in particular what else you would like to see. Your participation is entirely voluntary, you can choose not to answer any questions for any reason, and you can withdraw at any stage by closing your browser. Since the survey is anonymous, it will be impossible to withdraw data once you have submitted the survey. Only the FemQuant organising collective members will be able to view and analyse individual responses, including open text answers. The results of the survey will be used to inform the planning of FemQuant activities by the organising collective. We may share a summary of the results with the wider FemQuant network through the mailing list, on the website and on FemQuant social media. Any such communications will not include any potentially identifiable information, such as direct quotes from open text responses. To contact the organising collective, please email: femquant17@gmail.com

Google Docs
📣Calling all #FemQuant folks! 📣
Take part in our quick survey of the wider network to let us know what activities you'd like to see in 2024 + beyond! #feminist #quantitativemethods
https://forms.gle/gayFR3fh6ZJwMpeWA
Survey of FemQuant network

FemQuant was formed in 2017 following our first one-day conference focused on Feminist Approaches to Quantitative Social Science. Since then we have organised a number of events, including our monthly seminar series, and the organising collective has grown and become more international. The goal of our activities remains to bring together researchers who are using feminist theory and motivations in current quantitative, empirical research across the social sciences. We aim for the network to be a space and a community for researchers to discuss and debate the possibilities of a quantitative, feminist social science, and to learn from each other’s successes and difficulties in integrating feminist theory with quantitative methods. As an organising collective we would like to collect views from the wider network of what events and activities you find interesting and useful and in particular what else you would like to see. Your participation is entirely voluntary, you can choose not to answer any questions for any reason, and you can withdraw at any stage by closing your browser. Since the survey is anonymous, it will be impossible to withdraw data once you have submitted the survey. Only the FemQuant organising collective members will be able to view and analyse individual responses, including open text answers. The results of the survey will be used to inform the planning of FemQuant activities by the organising collective. We may share a summary of the results with the wider FemQuant network through the mailing list, on the website and on FemQuant social media. Any such communications will not include any potentially identifiable information, such as direct quotes from open text responses. To contact the organising collective, please email: femquant17@gmail.com

Google Docs

JOB: Wissenschaftliche/r Mitarbeiter/in (m/w/d) Digital Humanities
An der Philosophischen Fakultät der RWTH Aachen University (100% Beschäftigung, Tarifstufe TV-L13) im Bereich Digital Humanities. Die Stelle ist zunächst auf drei Jahre befristet.

#DigitalHumanities #TextAnalysis #QuantitativeMethods #LiteraryCorpora

https://www.accels.rwth-aachen.de/cms/ACCELS/Das-Zentrum/Offene-Stellenangebote/~bdbwqf/Wissenschaftliche-r-Mitarbeiter-in-m-w-/

Wissenschaftliche/r Mitarbeiter/in (m/w/d) Digital Humanities - RWTH AACHEN UNIVERSITY ACCELS - Deutsch

I often look at papers where authors used a lot of effort to shoehorn a #LongitudinalAnalysis into a trajectory or #MixedModels that do not quite the job the team wants.

Analysing longitudinal data (esp. w time-varying covariates) via G-Estimation is an alternative for consideration:
https://journals.sagepub.com/doi/full/10.1177/25152459231174029 #Tutorial

The underlying thinking is not entirely different, but often one needs only a little step / laterality to get a new view on an analysis problem.

#QuantitativeMethods #Rstats

The "Reviewer's guide to #QuantitativeMethods" provides a concise summary of what to learn about and how to plan & report studies employing 31 (2nd ed. apparently 35) methods: https://www.routledge.com/The-Reviewers-Guide-to-Quantitative-Methods-in-the-Social-Sciences/Hancock-Stapleton-Mueller/p/book/9781138800137

Since it is a book, it will be difficult for many to get access. Does anyone know whether at least the checklists have been published somewhere #OpenAccess?

And is there a #QualitativeMethods version?

#ResearchMethods #PeerReview #StudyDesign #ResearchWriting #AcademicEditing #SocialSciences

The Reviewer’s Guide to Quantitative Methods in the Social Sciences

The Reviewer’s Guide to Quantitative Methods in the Social Sciences provides evaluators of research manuscripts and proposals in the social and behavioral sciences with the resources they need to read, understand, and assess quantitative work. 35 uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The second edition of this valuable resource updates readers on

Routledge & CRC Press