Statistics in Linguistics and Communication
Course Description
This course focuses on statistical analysis of research data, centred around today’s standard method, which involves linear models, especially those with mixed effects (i.e., both “fixed effects” and “random effects” (e.g., participants drawn randomly from a population of people, and often also words or sentences drawn randomly from a language). If you are already familiar with some statistical techniques, you may have seen t-tests, correlation tests, and analysis of variance. During the course, these concepts turn out to be specific simplifying cases of mixed-effects modelling.
The course also addresses design issues that make the analyses suitable for your experiment, such as sampling, data collection, and reliability and validity of measurements. You apply these concepts, together with the analysis techniques, to theoretical, typological and applied research in your linguistic subdisciplines. Special attention is paid to statistical inference, i.e. correct use and formulation of statistical results.
You do not need prior knowledge of statistical methods. If you do have such knowledge, you may in fact have to unlearn some internalized ways of thinking.
Objectives
Upon successful completion of this course, you will
- have gained insight into the application of statistical and experimental methods in empirical research in linguistics;
- be able, given your data table, to compute the best mixed-effects model that describes your data by applying a technique called linear regression or a technique called logistic regression. The software you will be able to use for accomplishing that is R.
Further information can be found here.
NB: This is a course mainly meant for MA students. As a result, the amount of places for PhD researchers is extremely limited. Based on interest, we may organise a course specifically for PhD researchers next semester.
-
Teaching method and contact hours
Lecture/seminar: 2 x 3 hours per week.
The course consists of seminars that include a mix of lectures and practical exercises in which we discuss applications to different linguistic domains of your own choice.
As a student taking this course, you are required to make and submit homework exercises for most classes. As homework, you will typically create a literate R (“markdown”) script, i.e. a script in which you write both your text and your executable R code.
-
Study materials
- Class notes and homework notes provided via Canvas.
- A laptop, i.e., a portable computer on which you can install programs and download files (portable devices without these capabilities, such as “tablets” or “chrome books”, are not sufficient for taking part in all activities, including exams).
- You need to install RStudio and R.
- You are expected to bring your laptop to class; the costs of the course are minimal, but might include the cost of a new laptop battery that can last for three hours.
-
Assessment
- Homework | 30%
- Mid-term test in week 4 | 15%
- Computer test in week 8 | 20%
- Final written exam in week 8 | 35%
For the examination dates please consult the timetable on rooster.uva.nl.
-
Course registration
Course registration for the 2nd semester 2024-2025 opens in January 2025. You will be informed by your local research school before registration opens.
When registration is open, you will find the registrations links in the course fold-out. Please register for the waiting list if the course is full. This also signals the demand for the course.
NB: Read the course details closely before registering. This course requires you to turn in homework twice a week, and at least 168 hours of study.
-
Course Dates - February 2025
This course takes place during the second semester, and starts in February.
Lecturers
This course is taught by Titia Benders and Paul Boersma.