Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
10 credits
22.5 h
Q1
Teacher(s)
Paquot Magali;
Language
English
Prerequisites
One course of introduction to linguistics.
Main themes
Data analysis in a statistical software tool -- introduction:
- Quantitative analysis of linguistic data: descriptive and inferential statistics; introduction to regression analysis;
- Data visualization.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
At the end of the course, students will be able to select and use appropriate quantitative methods to analyze linguistic phenomena with the help of a statistical software tool. More practically, they will be able to use the statistical software tool R to explore linguistic data (descriptive statistics),represent data visually, and select the most appropriate statistics given the structure of their dataset |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
The course will be organized in two main parts:
- The first part of the course will provide a theoretical overview of statistics for linguistics and introduce the main concepts in statistics (descriptive statistics, inferencing, and modeling).
- The second part of the course will be more practical in nature. It will give students the opportunity to practise through exercises and a personal research project for which they will analyze real linguistic data.
Teaching methods
The teaching method will be a mix of traditional lectures and flipped classroom
Evaluation methods
The evaluation will be threefold:
- Continuous assessment (20%): participation in class activities, tests and exercises
- Written exam (40%)
- Research project (40%): individual research paper (or group research project that aims to analyze linguistic data for publication)
Other information
This course requires a good command of English (receptive and productive skills).
Online resources
Bibliography
- Gries, St. Th. 2013. Statistics for Linguistics with R. A Practical Introduction. 2nd edition. Berlin: De Gruyter Mouton.
- R codes
- Slides and additional chapters available on Moodle
Gries, St. Th. 2013. Statistics for Linguistics with R. A Practical Introduction. 2nd edition. Berlin: De Gruyter Mouton.
Howell, D. C. (2016). Fundamental statistics for the behavioral sciences. Nelson Education.
Teaching materials
- Gries, St. Th. 2013. Statistics for Linguistics with R. A Practical Introduction. 2nd edition. Berlin: De Gruyter Mouton.
- R codes
- Slides and additional chapters available on Moodle
Faculty or entity
FIAL
