Content overview
This page gathers all the important information about the course content and schedule. Each week is dedicated to a specific set of topics and you are expected to complete readings from the course textbook and homework by the beginning of the following week (so complete readings and homework of week 1 by the beginning of week 2, and so on).
Each week there will be a 50-minute lecture and a 2-hour workshop. The lecture covers the most important and/or difficult topics of the week, but you are expected to learn and understand all the topics of the week as treated in the readings from the course textbook. In the workshop you will further practice your R skills with group data challenges (with the exception of the workshop of week 1, where you will go through the R basics chapters in the textbook in class).
The Schedule overview below gives you an overview of each week, while the Weekly schedule is a more in-depth description of the activities and materials of each week.
Schedule overview
The following table is a schedule overview of the course, with the topic of the lecture, workshop and assessments due (WSR = weekly self-reflection). See Assessments for more info on assessment.
| 1 |
Research methods |
R basics |
WSR |
| 2 |
Inference and uncertainty |
Summarise, count and group data |
WSR |
| 3 |
Questionable Research Practices |
Plot basics |
WSR |
| 4 |
Bayesian inference |
Gaussian models with brms |
WSR |
| 5 |
Regression basics with brms |
Regression basics and posteriors draws |
WSR |
| 6 |
|
|
Formative test 1 |
| 7 |
Null Ritual |
Categorical predictors in regression |
WSR |
| 8 |
Bernoulli regression |
Bernoulli regression |
WSR |
| 9 |
Open research |
Multiple predictors and interactions |
WSR + Group project proposal |
| 10 |
Case study |
Group project |
WSR |
| 11 |
|
Group project |
WSR + Formative test 2 |
| 12 |
|
|
|
| 13 |
|
|
Final reflection + Group project |
Topics overview
In the following table, you can find a more detailed overview of the topics for each week, divided into research methods, statistics and R specific topics.
| 1 |
Research methods |
Quantitative data analysis |
RStudio and R basics |
| 2 |
|
Data summaries |
R scripts, read and summarise data |
| 3 |
Research cycle |
Data viz principles |
Quarto documents, data transformation and plottting |
| 4 |
Bayesian inference |
Probabilities and Gaussian models |
Gaussian models with brms |
| 5 |
|
Regression modelling (numeric predictor) and posterior draws |
Regression modelling with brms |
| 6 |
|
|
|
| 7 |
Null Ritual |
Regression (categorical predictor) |
Treatment and indexing |
| 8 |
|
Bernoulli regression |
Bernoulli regression |
| 9 |
Open research |
Regression (multiple predictors) and interactions |
|
| 10 |
|
Regression: more interactions |
|
Course textbook
The course textbook can be found at the following link (also available from the site menu): Quantitative Data Analysis for Linguists in R. The textbook has been written by Stefano, tailored specifically for this course.
Course data
The course data is available for download on the Data for Quantitative Methods in Linguistics website. For each data set, an entry gives detail about the study and the data set. When using data for the first time, you should read the related entry, abstract and skim the linked paper (if applicable) for context.
Week by week
Week 1: Quantitative methods and R basics
Questions
- What are the components of research methods?
- What makes a good research question and research hypothesis?
- What are the three steps of quantitative data analysis?
- What is the computational workflow of quantitative data analysis?
Skills
- Think critically about research methods and research questions.
- Master the basics of RStudio.
- Master the basics of the programming language R.
- Learn how to install and use R packages.
Course website
Carefully read the homepage.
Familiarise yourself with this Course content page, especially the Content overview and the Schedule overview.
Intake form
- You must complete the intake form before coming to the Tuesday lecture.
- The link to the form can be found on the Learn website.
Install R and RStudio
- For this course, you need to install both R and RStudio.
- You should install R and RStudio before coming to the workshop.
- NOTE: If you have installed either R or RStudio prior to January 2025, please make sure you first delete both R and RStudio from your laptop and then reinstall the latest versions.
- Instructions:
- Read Preface and Chapters 1, 2, 3 of the course textbook.
- Note that this week you are NOT expected to have completed this week’s readings before class, but you ARE expected to complete the week’s readings before the Workshop from Week 2 on! You must plan ahead so that by the Workshop in Week 2 you have completed the readings of Week 1 and Week 2. You will not be able to complete the workshops from Week 2 on without having done the readings of that week (which means that you very likely have to start the readings of following week in the previous week).
Go through Chapter 4 and Chapter 5 of the course textbook. You will start working in groups from next week since this week is probably easier to read and try things out solo, but feel free to work in group if it works for you!
The tutors will go around to ask how things are going and to answer questions or help.
Week 2: Inference, uncertainty and data summaries
Questions
What is inference and why do we need it?
Why are uncertainty and variability important?
What is and isn’t statistics?
What are summary measures?
Skills
Using R scripts to keep reproducible code.
Reading tabular data in R.
Summarise data with summarise() and count().
Get grouped summaries with group_by().
- Read the Week 2 chapters of the course textbook.
- Note that from this week on you ARE expected to have completed this week’s readings before this week’s Workshop! This means that you very likely have to start the readings of each week in the previous week.
Week 4: Probability distributions and Gaussian models
Questions
What are probabilities and probability distributions?
How do we describe and visualise probability distributions?
How do we use Gaussian probability distributions to estimate a mean and standard deviation?
What is the Bayesian approach to probability and inference?
Skills
Produce density plots.
Use pnorm() and qnorm() to obtain probabilities and quantiles of Gaussian distributions.
Fit Bayesian Gaussian models with brms.
Report results from Bayesian Gaussian models.
- Read the Week 4 chapters of the course textbook.
- Remember that you ARE expected to have completed this week’s readings before this week’s Workshop! This means that you very likely have to start the readings of each week in the previous week.
Week 5: Regression models
Questions
What are regression models and what are they for?
How do we interpret intercept and slope in a regression model?
Why do we need Markov Chain Monte Carlo to estimate models?
How do we use MCMC draws from a model fit?
Skills
Fit regression models of the form \(y \sim x\) with brms in R.
Interpret the regression coefficients table of the model summary.
Extract and plot MCMC draws.
Report results from regression models.
- Read the Week 5 chapters of the course textbook.
- Remember that you ARE expected to have completed this week’s readings before this week’s Workshop! This means that you very likely have to start the readings of each week in the previous week.
Week 6: Catch-up week
This week there will be no classes (no lecture nor workshop) for you to catch up with the materials for Week 1-5 if you need to and/or revise them.
Note that the workshop’s room is still booked at the usual times so you can use it a group study space if you wish.
You should complete the Formative Test 1 this week, to check your learning before moving on onto the second part of the course.
The link to the test is available on the Learn site of the course.
Week 7: Categorical predictors and frequentist statistics
Week 8: Binomial/Bernoulli and log-normal regression models
Week 9: Predictor interactions in regression models