Short course and keynote on statistical methods at Ghent Summer School on Methods in Language Sciences

I will be teaching an in-person course on linear mixed modeling at the summer school at Ghent (below) August 2022.

The summer school home page: https://www.mils.ugent.be/

1. 2.5 day course: Introduction to linear mixed modelling for linguists

When and where: August 18, 19, 20, 2022 in Ghent.

 Prerequisites and target audience

The target audience is graduate students in linguistics.

I assume familiarity with graphical descriptive summaries of data of the type

encountered in linguistics; the most important theoretical distributions 

(normal, t, binomial, chi-squared); description of univariate and bivariate data

(mean, variance, standard deviation, correlation, cross-tabulations);

graphical presentation of univariate and bivariate/multivariate data

(bar chart, histogram, boxplot, qq-plot, etc.);

point estimators and confidence intervals for population averages

with normal data or large samples;

null hypothesis significance testing;

t-test, Chi-square test, simple linear regression.

A basic knowledge of R is assumed.

Curriculum:

I will cover some important ideas relating to linear mixed models

and how they can be used in linguistics research. I will loosely follow

my textbook draft: https://vasishth.github.io/Freq_CogSci/

Topics to be covered: 

- Linear mixed models: basic theory and applications

- Contrast coding

- Generalized Linear Mixed Models (binomial link)

- Using simulation for power analysis and for understanding one’s model

2. Keynote lecture

 Using Bayesian Data Analysis in Language Research

Shravan Vasishth

Bayesian methods are becoming a standard part of the toolkit for
psycholinguists, linguists, and psychologists. This transition has
been sped up by the arrival of easy-to-use software like brms, a
front-end for the probabilistic programming language Stan. In this
talk, I will show how Bayesian analyses differ from frequentist
analogues, focusing on the linear mixed model. I will illustrate the
main advantages of Bayes: a direct,  nuanced, and conservative answer
to the research question at hand, flexible model specification, the
ability to incorporate prior knowledge in the model, and a focus on
uncertainty quantification.

References
Daniel J. Schad, Bruno Nicenboim, Paul-Christian Bürkner, Michael
Betancourt, and Shravan Vasishth. Workflow Techniques for the Robust
Use of Bayes Factors. Psychological Methods, 2022.
https://doi.apa.org/doiLanding?doi=10.1037%2Fmet0000472

Shravan Vasishth and Andrew Gelman. How to embrace variation and
accept uncertainty in linguistic and psycholinguistic data analysis.
Linguistics, 59:1311--1342, 2021.
https://www.degruyter.com/document/doi/10.1515/ling-
2019-0051/html

Shravan Vasishth. Some right ways to analyze (psycho)linguistic data.
Submitted, 2022.
https://osf.io/5wzyg/