Statistics for Linguistics

lfial2260  2024-2025  Louvain-la-Neuve

Statistics for Linguistics
5.00 credits
22.5 h
Q1
Teacher(s)
Language
English
Prerequisites
One course of introduction to linguistics.
Main themes
Quantitative analysis of linguistic data with R
  • Data visualization
  • Descriptive statistics : definitions ; computing and representation
  • Inferential statistics: main concepts
  • Basic statistical analyses : frequency comparisons, means comparisons, non-parametric testing, correlations
  • (Theoretical) introduction to regression modelling.
Learning outcomes

At the end of this learning unit, the student is able to :

At the end of the course, students will be able to select and use basic 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 (among basic approaches) given the structure of their dataset
They will also be able to understand a scientific article based on more sophisticated statistical techniques (e.g. regression modelling), and to critically examine the results of a quantitative study.
 
Content
Quantitative analysis of linguistic data with R (descriptive statistics, inferential statistics, data visualization)
Teaching methods
The teaching method will be a mix of traditional lectures and exercises
Evaluation methods
The evaluation will be twofold:
  • Continuous assessment (compulsory exercises in the form of quizzes and assignments) (30% of the grade). The compulsory exercises are based on standard questions such as those used in the exam. These questions are then corrected with the students, specifying the level of mastery and rigor expected, so that students can see what is expected and adapt their study of the subject accordingly.
  • Written exam (70%)
In accordance with Article 72 of the Règlement général des études et des examens (RGEE), the course instructor may propose to the jury that a student who has not handed in his or her exercises on time be refused registration for the examination.
In case of resit, the evaluation will be based on a written exam only (100%) 
Generative artificial intelligence (AI) must be used responsibly and in accordance with the practices of academic and scientific integrity. Since scientific integrity means quoting sources, the use of AI must always be reported. The use of artificial intelligence for tasks where it is explicitly forbidden will be considered cheating.
Other information
This course requires a good command of English (receptive and productive skills).
Bibliography
  • Field, A. et Miles, J. and Field, Z. (2012). Discovering Statistics Using R. London : Sage Publications.
  • Gries, St. Th. (2021). Statistics for Linguistics with R. A Practical Introduction. 3rd edition. Berlin: De Gruyter Mouton.
  • Howell, D. C. (2016). Fundamental statistics for the behavioral sciences. Nelson Education.
Teaching materials
  • Gries, St. Th. 2021. Statistics for Linguistics with R. A Practical Introduction. 3rd edition. Berlin: De Gruyter Mouton.
  • Slides and additional chapters available on Moodle
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Linguistics