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Reposted by Anna Langener

Elizabeth W. Lampe, Ph.D. @elizabethwlampe.bsky.social · 4d

‼️job alert ‼️
@emilypresseller.bsky.social is an inspiring researcher and a fantastic mentor-- her lab will be such an incredible launching pad for a budding future scientist!!!

Emily Presseller @emilypresseller.bsky.social · 4d

I am excited to share that I began as an assistant professor at the Center for Technology and Behavioral Health at Dartmouth last week. I am hiring a research coordinator to join my team ASAP! Please share with anyone who might be interested!
searchjobs.dartmouth.edu/postings/83064

Research Coordinator

The Scalable, Accessible Treatments for Eating Disorders and Related Psychopathology (SATED) research team at Dartmouth College is pleased to invite applications to join our exciting and supportive re...

searchjobs.dartmouth.edu

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Reposted by Anna Langener

Washington State Health Care Authority @wa-health-care.bsky.social · Sep 7

The 988 Lifeline isn’t just for thoughts of suicide. 988 Lifeline crisis counselors provide 24/7/365 support for any type of emotional distress. Get compassionate help anytime, anywhere. Call or text 988 or chat online at 988Lifeline.org.
#988Day

Call. Text. Chat. 988 is here for you - anytime, anywhere. The 988 Lifeline provides judgment-free, compassionate support for anyone experiencing: emotional distress, mental health challenges, problems with substance use, loneliness, thoughts of suicide or self harm. You'll be connected with a caring, skilled 988 counselor who can provide culturally competent support.

Why 988 matters: the 988 Lifeline connects people to 988 counselors across a network of more than 200 local crisis contact centers, offering support rooted in local communities.

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Reposted by Anna Langener

Association for Psychological Science @psychscience.bsky.social · Aug 5

AMPPS Call for Papers: Replicability and Reproducibility in Methodological Research. Proposals due September 15. @jkflake.bsky.social

www.psychologicalscience.org

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Reposted by Anna Langener

JK Flake @jkflake.bsky.social · Jul 18

Are you working on issues of replication and reproducibility of methodological research in psychology? Consider submitting a proposal to my call for papers, happy to chat about your ideas or answer any questions! www.psychologicalscience.org/publications...

<em>AMPPS</em> Call for Papers on Replicability and Reproducibility in Methodological Research

Methodological reform has shaped the last decade of psychological research. Researchers have undertaken replication studies, journals and funders have emphasized registration and data sharing, and aut...

www.psychologicalscience.org

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Reposted by Anna Langener

Laura Bringmann 🟥 @bringmannlaura.bsky.social · Jun 26

Want to learn about dynamic modeling for daily diary, experience sampling, ecological momentary assessment data? 😎

Register for our online course ‘Modeling the dynamics of intensive longitudindal data’ which starts in October 2025! 🤩

utrechtsummerschool.nl/courses/data...

Modelling the Dynamics of Intensive Longitudinal Data (e-learning) 2025 | Utrecht Summer School

This online course covers how time series models can be used to model the dynamics of intensive longitudinal data (ILD).

utrechtsummerschool.nl

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Reposted by Anna Langener

The ESM Item Repository @esmrepository.bsky.social · May 5

New blog post! 📣 Ever wanted to do more with your open-text experience sampling items, but were unsure 🤔 how to code the responses? @annalangener.bsky.social & @mariestadel.bsky.social show us how it's done with their new Shiny app for open-text ESM data: esmitemrepositoryinfo.com/blog-posts#M...

Blog posts | ESM Item Repository

esmitemrepositoryinfo.com

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Reposted by Anna Langener

Björn Siepe @bsiepe.bsky.social · Apr 28

Can we use features of dynamic networks (e.g. centrality) to improve treatment selection and outcome prediction?

New preprint on the topic: We highlight the role of uncertainty & introduce a Bayesian multilevel approach for uncertainty quantification of network features 🧵
osf.io/preprints/ps...

Image with three main boxes with recommendations for applied researchers on conceptualization, estimation, and interpretation when using network features.
Conceptualization:
- Discuss why a network feature should be relevant for a given treatment or outcome
- Consider plausible effect size and sample size needed to detect an association with a distal outcome

Estimation:
- Carefully choose preprocessing and network estimation methods
- Follow good practices for predictive models (e.g., cross-validation, out-of-sample validation)

Interpretation:
- Consider uncertainty in node selection and network feature regression
- Compare network features with simpler time series features (e.g., person-specific mean or SD)

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Reposted by Anna Langener

Laura Bringmann 🟥 @bringmannlaura.bsky.social · Apr 22

Looking for a PhD position? Come work with me! 🤩
In this PhD project, you will study how decisions in medicine (at the intensive care) can be improved using tools like algorithmic advice in both academic and hospital settings.
Talking Dutch is a plus! 😊
www.academictransfer.com/en/jobs/3514...

PhD Gut Feeling or Algorithm? Predicting Patient Outcomes in Critical Care

PhD Gut Feeling or Algorithm? Predicting Patient Outcomes in Critical Care (0.8-1.0 FTE) Do you want to cont

www.academictransfer.com

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Anna Langener @annalangener.bsky.social · Mar 5

7/ Want to learn more? Join me for a workshop at the SIPS online conference, where I’ll dive deeper into these topics! ✨ #SIPS2025

Anna Langener @annalangener.bsky.social · Mar 5

6/ To address these pitfalls, we present recommendations for aligning validation and evaluation strategies with the intended use case scenario and created a tool to help researchers investigate whether their strategies and goals are misaligned: annalangener.shinyapps.io/Justintime/

Just in Time or Just a Guess? Validating Prediction Models Based on Longitudinal Data

annalangener.shinyapps.io

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Anna Langener @annalangener.bsky.social · Mar 5

5/ Third, selecting appropriate baseline models is key. Some models may look effective (e.g., AUC = 0.77) but actually underperform compared to simple baselines (e.g., AUC = 0.96). ⚖️

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Anna Langener @annalangener.bsky.social · Mar 5

4/ Second, ensuring adequate variability in the outcome variable is crucial. If outcomes are stable, frequent predictions may offer little practical benefit for JITAI.

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Anna Langener @annalangener.bsky.social · Mar 5

3/ Centering predictor variables within individuals can improve within-person accuracy but may reduce overall performance.

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Anna Langener @annalangener.bsky.social · Mar 5

2/ First, models may perform well overall (AUC = 0.77), but their ability to predict within-person change can be much lower (AUC = 0.56, SD = 0.11). For JITAIs, this will prevent the model from identifying intervention delivery moments and will only discriminate between people.

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Anna Langener @annalangener.bsky.social · Mar 5

1/ Many researchers are focused on building prediction models for JITAIs. But a major challenge is the mismatch between model development, evaluation, and application. We use simulations to illustrate three pitfalls.

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Anna Langener @annalangener.bsky.social · Mar 5

✨ Excited to share our new preprint: osf.io/preprints/os... ✨

In this paper, Nick Jacobson and I dive into the challenges researchers face when developing prediction models for just-in-time adaptive interventions (JITAI). 🧵👇

#EMA #JITAI #CrossValidation #PassiveSensing

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Anna Langener @annalangener.bsky.social · Mar 5

5/ Third, selecting appropriate baseline models is key. Some models may look effective (e.g., AUC = 0.77) but actually underperform compared to simple baselines (e.g., AUC = 0.96). ⚖️

Anna Langener @annalangener.bsky.social · Mar 5

4/ Second, ensuring adequate variability in the outcome variable is crucial. If outcomes are stable, frequent predictions may offer little practical benefit for JITAI.

1

Anna Langener @annalangener.bsky.social · Mar 5

3/ Centering predictor variables within individuals can improve within-person accuracy but may reduce overall performance.

1

Anna Langener @annalangener.bsky.social · Mar 5

2/ First, models may perform well overall (AUC = 0.77), but their ability to predict within-person change can be much lower (AUC = 0.56, SD = 0.11). For JITAIs, this will prevent the model from identifying intervention delivery moments and will only discriminate between people.

1

Anna Langener @annalangener.bsky.social · Mar 5

1/ Many researchers are focused on building prediction models for JITAIs. But a major challenge is the mismatch between model development, evaluation, and application. We use simulations to illustrate three pitfalls.

1

Anna Langener @annalangener.bsky.social · Mar 5

7/ Want to learn more? Join me for a workshop at the SIPS online conference, where I’ll dive deeper into these topics! ✨ #SIPS2025

Anna Langener @annalangener.bsky.social · Mar 5

6/ To address these pitfalls, we present recommendations for aligning validation and evaluation strategies with the intended use case scenario and created a tool to help researchers investigate whether their strategies and goals are misaligned: annalangener.shinyapps.io/Justintime/%....

https://annalangener.shinyapps.io/Justintime/💡

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Anna Langener @annalangener.bsky.social · Mar 5

5/ Third, selecting appropriate baseline models is key. Some models may look effective (e.g., AUC = 0.77) but actually underperform compared to simple baselines (e.g., AUC = 0.96). ⚖️

1

Anna Langener @annalangener.bsky.social · Mar 5

4/ Second, ensuring adequate variability in the outcome variable is crucial. If outcomes are stable, frequent predictions may offer little practical benefit for JITAI.

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