Statistics for Linguistics

Teacher(s)

Paquot Magali;

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, correlationsques, correlations
  
• (Theoretical) introduction to regression modelling and classification trees
  

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.

Other information

This course requires a good command of English (receptive and productive skills).

Online resources

https://moodleucl.uclouvain.be/course/view.php?id=12097

Bibliography

• Field, A. et Miles, J. and Field, Z. (2012). Discovering Statistics Using R. London : Sage Publications.
• 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.
• Slides and additional chapters available on Moodle

Faculty or entity

> FIAL