Short course and keynote on statistical methods at Ghent Summer School on Methods in Language Sciences
This article is originally published at https://vasishth-statistics.blogspot.com/
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.
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 ResearchShravan 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
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.
Shravan Vasishth and Andrew Gelman. How to embrace variation and
accept uncertainty in linguistic and psycholinguistic data analysis.
Linguistics, 59:1311--1342, 2021.
Shravan Vasishth. Some right ways to analyze (psycho)linguistic data.
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