Welcome to the Eighth Summer School on Statistical Methods for Linguistics and Psychology, 9-13 September 2024






Application form

Applications closed April 1, 2024. Decisions will be announced April 15, 2024.

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: 9-13 September 2024.
  • 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.
  • Application period: Oct 1, 2023 to April 1, 2024.
  • Schedule: coming soon.

Brief history of the summer school, and motivation

The summer school was started by Shravan Vasishth in 2017, as part of a methods project funded 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: 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. Stephan Lewandowsky
  2. Julia Haaf
  3. Henrik Singmann

    4. Courses

    • Introduction to Bayesian data analysis (maximum 30 participants). Taught by Shravan Vasishth, and Anna Laurinavichyute.

      • You can decide whether this course is appropriate for you by looking at the 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. We will cover these topics within the context of an applied Bayesian workflow that includes exploratory data analysis, model fitting, and model checking using simulation.
      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 Textbook: here. We will work through the first six chapters, plus the chapter on 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 here. We will start from Part III of the book (Advanced models with Stan). Participants are expected to be familiar with the first five chapters.

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

      • Participants will be expected to have used linear mixed models before, to the level of the textbook by Winter (2019, Statistics for Linguists), 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, Phillip Alday, and Doug Bates.

    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. Some of them are: (a) new and more efficient computational implementation, (b) speed — needed for, e.g., complex designs and power simulations, (c) more flexibility for selection of parsimonious mixed models, and (d) more flexibility in taking into account autocorrelations or other dependencies — typical EEG-, fMRI-based time series (under development). 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. The course will be taught in a hybrid IDE. There is already the option to execute R chunks from within Julia, meaning one needs Julia primarily for execution of MixedModels.jl commands as replacement of lme4. There is also an option to call MixedModels.jl from within R and process the resulting object like an lme4-object. Thus, much of pre- and postprocessing (e.g., data simulation for complex experimental designs; visualization of partial-effect interactions or shrinkage effects) can be carried out in R.

    Course Materials
    1. Embrace uncertainty
    2. Github repo from 2023
    3. Github repo from 2022
    4. Github repo from 2021
    5. Github repo from 2020
    6. Publications using MixedModel.jl


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

This summer school is funded by the DFG and is part of the SFB 1287, “Variability in Language and Its Limits”.