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.
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 VasishthBayesian 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
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/docu
2019-0051/html
Shravan Vasishth. Some right ways to analyze (psycho)linguistic data.
Submitted, 2022.
https://osf.io/5wzyg/
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This article is originally published at https://vasishth-statistics.blogspot.com/
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