Application form

Applications are now closed. Decisions will be announced April 15, 2025. If you don't receive a decision by April 16, 2025, please write to us.

Schedule and important organizational details

Download the schedule as a pdf document here. Download this pdf document for some important organizational information.

Important notice

The summer school itself is free. However, many of the bakeries and food places near the summer school location still require cash payment for food; credit/debit cards and EC cards won't be accepted so please bring some cash with you.

Dates, location

• Dates: 25-29 August 2025.
• Times: 9AM-5PM daily.
• Location: The summer school will be held at the Griebnitzsee campus in Potsdam, at Haus 6. For train connections, consult bvg.de; the train station near the campus is called Griebnitzsee Bhf (short for Bahnhof, which means train station).
• Application period: Oct 10, 2024 to April 1, 2025.
• Schedule: Download the schedule as a pdf document here..

Brief history of the summer school, and motivation

The summer school was started by Shravan Vasishth in 2017, and was funded until 2024 as part of a methods project within the SFB 1287. The summer school aims to fill a gap in statistics education, specifically within the fields of linguistics and psychology. One goal of the summer school is to provide comprehensive training in the theory and application of statistics, with a special focus on the linear mixed model. Another major goal is to make Bayesian data analysis a standard part of the toolkit for the linguistics and psychology researcher. Over time, the summer school has evolved to have at least four parallel streams: foundational and advanced courses in frequentist and Bayesian statistics. These may be expanded to more parallel sessions in future editions. We typically admit a total of 120 participants (in 2019, we had some 450 applications). In addition to the all-day courses, we regularly invite speakers to give lectures on important current issues relating to statistics. Previous editions of the summer school: 2024, 2023, 2022, 2021, 2020, 2019, 2018, 2017.

Code of conduct

All participants will be expected to follow the (code of conduct, taken from StanCon 2018. In case a participant has any concerns, please contact any of the following instructors: Audrey Bürki, Anna Laurinavichyute, Shravan Vasishth, Bruno Nicenboim, or Reinhold Kliegl.

Keynote speakers

1. Tuesday (26 August 2025): Ben Bolker
   Title: Progress and challenges in open-source multilevel modeling: opinions of an alien
   Abstract: In parallel with the revolution in machine learning and AI, more traditional multilevel models are rapidly becoming more flexible, powerful, and readily available in open-source software. I will try to present a view that is eclectic and 'alien', i.e. covering a range of topics from a perspective that differs from that of most of the audience or other method developers (ecology, little formal training in statistics). I'll briefly review of the current landscape of mixed modeling tools, exciting new directions, and open challenges. I will focus on methods available in R but compare these tools with those available in Julia and Python, as well as in some commercial software. Other topics may include model selection, troubleshooting (convergence and singular-fit problems), and the use of latent variables to fit nonlinear patterns in data.
2. Thursday (28 August 2025): Reinhold Kliegl
   Title: Aligning theory and effects with non-canonical specifications of linear mixed models
   Abstract: Inferential statistics in cognitive, behavioral, and social sciences increasingly rely on (generalized) linear mixed models and Bayesian statistics. Their canonical application often misses out on insights to be gained by theory-guided considerations at different levels of model specification. Theory-based non-canonical specifications relate to (1) the relevance of transformations of measures and covariates, (2) nested vs crossed designs for fixed effects and/or the random-effect structure, (3) regression discontinuity designs, (4) (moderation by) quadratic, cubic, or spline covariates, (5) selection against degenerate (G)LMMs,(7) correlation parameters for levels and effects, (8) some caveats about their interpretation, and (9) conditional modes for prediction of individual (or other group) differences in (quasi-)experimental effects. Using examples from sport science (e.g., Bähr et al., 2025; Teich et al., 2025), I illustrate for a subset of the above how sex, age, body constitution, secular trends (incl. pandemic Covid-19 effects), school social status, and interactions between these effects relate to the physical fitness of primary-school children. With a few exceptions, these non-standard specifications are relevant for a broad range statistical models (e.g., Bayesian, structural-equation, latent growth curve models).
   1. Bähr, F., et al. (2025). Impact of age, sex, body constitution, and the COVID-19 pandemic on the physical fitness of 38,084 German primary school children. Sci Rep 15, 11300. https://doi.org/10.1038/s41598-025-95461-5
   2. Teich, P., et al. (2025). Association of school social status with COVID-19 pandemic-related changes and post-pandemic rebounds of children’s physical fitness. Sports Med - Open 11, 41. https://doi.org/10.1186/s40798-025-00838-5.

Courses

• Introduction to Bayesian data analysis (maximum 30 participants). Taught by Julia Haaf and Nicole Cruz.
  You can decide whether this course is appropriate for you by looking at a previously taught online version of this course (videos are available): see here.
  
  This course is an introduction to Bayesian modeling, oriented towards linguists and psychologists. Topics to be covered: Introduction to Bayesian data analysis, Linear Modeling, Hierarchical Models, and Bayes factors. We will cover these topics within the context of an applied Bayesian workflow that includes exploratory data analysis, model fitting, and model checking using prior and posterior predictive checks.
  Prerequisites: Participants are expected to be familiar with frequentist methodology (what is taught in the foundations course, see below, as well as this online textbook: here), be relatively fluent in R usage, and must have some experience in data analysis, particularly with the R library lme4. Basic high school (pre-calculus) arithmetic and mathematical fluency is assumed; for example, you should know what a log is, what an exponent is, and be able to solve for y in x=log(y/(1-y)). If you are unfamiliar with frequentist methods, we suggest taking the introductory frequentist course listed below.
  Course Materials The following freely available online textbook will be used: see here. A hard-copy of the book is available here. The course will cover chapters 1-5, and possibly also chapter 13 (Bayes factors).
  
  
  
• Advanced Bayesian data analysis (maximum 30 participants). Taught by Bruno Nicenboim and Himanshu Yadav

This course assumes that participants have some experience in Bayesian modeling already using brms and want to transition to Stan to learn more advanced methods and start building simple computational cognitive models. Participants should have worked through or be familiar with the material in the first five chapters of our book draft: Introduction to Bayesian Data Analysis for Cognitive Science. In this course, we will cover Parts III to V of our book draft: model comparison using Bayes factors and k-fold cross validation, introduction and relatively advanced models with Stan, and simple computational cognitive models.
Course Materials Textbook: see here. A hard-copy of the book is available here. We will start from Part III of the book (Advanced models with Stan). Participants are expected to be familiar with the first six chapters.


• Foundational methods in frequentist statistics (maximum 30 participants). Taught by Daniel Schad, and João Veríssimo.

Participants will be expected to have used linear mixed models before, and want to acquire a deeper knowledge of frequentist foundations, and understand the linear mixed modeling framework more deeply. Participants are also expected to have fit multiple regressions. We will cover model selection, contrast coding, with a heavy emphasis on simulations to compute power and to understand what the model implies. We will work on (at least some of) the participants' own datasets. This course is not appropriate for researchers new to R or to frequentist statistics.
Course Materials Textbook draft here.


• Advanced methods in frequentist statistics with Julia (maximum 30 participants). Taught by Reinhold Kliegl, and Phillip Alday.

Applicants must have experience with linear mixed models and be interested in learning how to carry out such analyses with the Julia-based MixedModels.jl package) (i.e., the analogue of the R-based lme4 package). MixedModels.jl has some significant advantages stemming from a faster implementation incorporating several algorithmic advances. The speed improvements make it easier to fit larger and more complex models as well as perform the repetitive model fitting necessary for power simulation and bootstrapping.

We do not expect profound knowledge of Julia from participants; the necessary subset of knowledge will be taught on the first day of the course. We do expect a readiness to install Julia and the confidence that with some basic instruction participants will be able to adapt prepared Julia scripts for their own data or to adapt some of their own lme4-commands to the equivalent MixedModels.jl-commands. There is good interoperability between R and Julia (with RCall on the Julia side and JuliaCall on the R side) so that users can continue using R as necessary for e.g. data wrangling and plotting.

Previous editions' course Materials
• Course materials, including lecture notes and worked examples. Please also see this course website for instructions for installing necessary software.
• Embrace Uncertainty in progress book on mixed effects models using Julia. Several chapters started off as lectures at past SMLPs!

Fees and accommodation

The summer school is free. Participants who are accepted are expected to arrange their own accommodation. We strongly advise participants to find a place to stay near Griebnitzsee campus, and not in Berlin. The reason is that German train personnel tend to go on strike every year around the time of the summer school. You will be better off if you can get easily to the Griebnitzsee campus.

Contact details

For any questions regarding this summer school that have not been addressed on this home page already, please contact Shravan Vasishth.

Funding

Partly funded by COST Action MultiplEYE (CA21131), supported by COST (European Cooperation in Science and Technology).