# LING 683 Lecture notes: Fall 2024

# Preface

These are lecture notes for LING 683, *Advanced Quantative Methods*, taught in McGill Linguistics in Fall 2024. The notes are organized as a Quarto book.

The last update to these notes was on the “Published” date above.

**These notes assume you have done readings** indicated at the beginning of the chapter, each of which corresponds roughly to a week of the course. The course schedule, with readings, is here.

These notes include:

**Applications to linguistic data**of concepts from the reading to linguistic data**Practical illustration**of topics from the reading**Exercises**: to be done in class, or on your own time**Homework problems**: to be turned in for assessment.

Although I’m writing these notes with LING 683 in mind, I hope that they can (eventually) also be of use as a study guide for language scientists interested in expanding their quantitative toolbox. Here is the introduction to the course syllabus, which should give a sense of whether these materials could be helpful for you:

“This is a second course on quantitative methods for analyzing linguistic data. It follows LING 620, where we focused on regression modeling using R, up to linear and logistic mixed-effects models. Using this as a starting point, our goals are to broaden your conceptual knowledge and methodological toolkit of quantitative methods, in order to broaden the research questions you can ask and the types of data you can analyze. This term we will cover (a) Bayesian data analysis and (b) generalized additive (mixed) models, along the way introducing (c) model types beyond linear and logistic (e.g. multinomial, Poisson) and (d) possibly other current methods (e.g. functional data analysis). These methods are increasingly used to analyze linguistic data, but are relatively new to language scientists, and standard tools and best practices for practical applications are evolving. A theme of the course is practical application, and a primary goal is developing a sufficiently strong basis in (a)–(c) that you will be able to figure out the quantitative methods needed to analyze your data in the future.”