Welcome to the Fourth Summer School on Statistical Methods for Linguistics and Psychology, 7-11 September 2020




Application, dates, location

  • Dates: 7-11 September 2020.
  • Location: The summer school will be held at the Griebnitzsee campus of the University of Potsdam; this is about 15-20 minutes away from Berlin zoo station by train (this of course assumes that the trains are working as they are supposed to!). Lectures will be held in Haus 6. Please use bvg.de for planning your travel (by train or bus).
  • Application period: 15 Jan to 1 April 2020. Please apply by filling out this form.

Brief history of the summer school, and motivation

The SMLP 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: beginning 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: 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, Shravan Vasishth, Bruno Nicenboim, João Veríssimo, or Reinhold Kliegl.

Invited lecturers

  • Douglas Bates (co-instructor for Advanced methods in frequentist statistics with Julia)
  • Jonah Gabry (Instructor for Introduction to Bayesian regression modeling with rstanarm).
  • Robert Grant (Lectures on Visualization)
  • Phillip Alday (Advanced methods in frequentist statistics with Julia)

Invited keynote speakers

  • Christina Bergmann (Title: The "new" science: transparent, cumulative, and collaborative)
  • Jeff Rouder (Modeling individual differences)
  • Jennifer Tackett (Thinking Critically and Embracing Uncertainty: Approaches to Theory Specification and Testing in Psychological Science)

Curriculum

We offer foundational/introductory and advanced courses in Bayesian and frequentist statistics. When applying, participants are expected to choose only one stream.
  • Introduction to Bayesian regression modeling with rstanarm (maximum 30 participants). Taught by Jonah Gabry, assistant: Shravan Vasishth
  • (This course is an introduction to Bayesian modeling.) This course assumes familiarity with R and with standard frequentist statistical tools like the generalized linear model, linear mixed model, and ANOVA. Topics to be covered: Introduction to Bayesian data analysis, GLMs for Binary/Binomial Data, GLMs for Continuous Data, GLMs for Count Data, Generalized (Non-)Linear Models with Group-Specific Terms, Bayesian ANOVA Models, Regularized Linear Models, Ordinal Regression Models, Hierarchical Partial Pooling for Repeated Binary Trials. We will cover these topics within the context of an applied Bayesian workflow that includes exploratory data analysis, model fitting, and model checking.

  • Advanced Bayesian data analysis (maximum 30 participants). Taught by Bruno Nicenboim and Shravan Vasishth
  • This course assumes that participants have some experience in Bayesian modeling already (using JAGS/WinBUGS/Stan, or some other probabilistic programming language) and want to learn more advanced methods. Participants should have worked through or be familiar with the material in the first four chapters of our book draft: Introduction to Bayesian Data Analysis for Cognitive Science. We will cover hierarchical modeling, multinomial processing trees, measurement error models, meta-analysis, mixture models, and using Bayes factors and k-fold cross validation.

  • 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 model more deeply. Participants are expected to have fit multiple regressions. We will cover model selection, contrast coding, and using simulations to compute power. 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.

  • Advanced methods in frequentist statistics with Julia (maximum 30 participants). Taught by Phillip Alday, Douglas Bates, and Reinhold Kliegl
Applicants must have experience with linear mixed models and be interested in learning how to carry out such analyses with the Julia-based MixedModels package) (i.e., the analogue of the R-based lme4 package). Julia MixedModels has some significant advantages. Some of them are: (a) new and more efficient computational implementation, (b) speed — needed for, e.g., complex designs and the computation of statistical power, (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 lmer()-commands to the equivalent MixedModels()-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 command as replacement of lmer(). There is also an option to call Julia MixedModels 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 and will be carried out in the RStudio / R environment.

Fees

The fee of 30 Euros will be used to (among other things) provide coffee and snacks. There will be no other fees for participating, but since seats are limited, candidates must apply to be accepted. Participants will need to find and pay for their own accommodation.

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”.