Statistics for Linguistics

Teacher(s)

Paquot Magali;

Language

English
> French-friendly

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 :
1	Understand why statistics are necessary in linguistics, and how they can be used to test hypotheses, model linguistic phenomena and validate empirical conclusions.
2	Define the basic concepts of descriptive and inferential statistics (e.g. mean, standard deviation, basic concepts of hypothesis testing)
3	Organize and clean linguistic data in preparation for statistical analysis
4	Use graphs and tables to represent linguistic data
5	Interpret graphs and tables in the context of linguistic analysis
6	Calculate and interpret measures of central tendency and dispersion to represent linguistic data
7	Perform simple statistical tests and interpret results
8	Use R to perform basic descriptive and inferential statistics
9	Clearly present the results of statistical analyses
10	Understand a scientific article using (basic) statistics and look critically at the results of a quantitative analysis.
This teaching unit contributes to the development and mastery of the following skills and knowledge from the School of Languages and Arts syllabus (see ELAL AA table):   1.4 ; 2.3 ; 2.4; 2.6 ; 3.1 ; 4.5 ; 5.1 ; 5.3

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). 
• Written exam (70%)

Course participation
In accordance with Article 72 of the Règlement général des études et des examens (RGEE), the course instructor may propose to the examination board that a student be denied registration for the January or September exam if they have not participated regularly in learning activities and/or have failed to submit assignments within the required deadlines.
Second exam registration
In case of resit, the student will be assessed solely on the basis of a written exam, which will account for 100% of the final grade.
Use of generative artificial intelligence
Generative artificial intelligence (AI) must be used responsibly and in accordance with the practices of academic and scientific integrity. Since scientific integrity requires the citation of sources, the use of AI must always be reported. Using AI for tasks where it is explicitly prohibited will be considered an act of academic dishonesty/cheating.

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., 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

> FIAL