Statistics for Linguistics

Teacher(s)

François Thomas;

Language

French

Prerequisites

One course of introduction to linguistics.

Main themes

Data collection: descriptive and experimental methods, reliability and validity, sampling procedures.
Descriptive statistics: definitions, graphical representation, numerical summaries.
Using a statistical software
Inferential statistics : main concepts.
Basic statistical analyses and tests: frequency analysis (categorical data), testing hypotheses about means, correlation and regression, non parametric tests. Advanced statistical analyses and tests: interrater agreement measures, multivariate descriptive techniques, regression models for contingency tables, '.

Learning outcomes

At the end of this learning unit, the student is able to :
1	At the end of the course, the student will be able to use the main statistical tools and concepts of quantitative linguistics. He will be able to choose appropriate methods for specific research objectives and to use it in the framework of linguistic research. Most of all, the student will be able to make a critical review of the results obtained by a quantitative analysis. He will also be trained to use a statistical analysis software.

Content

The organization of the course is twofold :

1. The first part of the course consists in a theoretical approach in the field of textual data statistical analysis introducing the main concepts in statistics (descriptive statistics, inference, and modeling).
2. The second part of the course will provide a practical approach of the field. It will give the students the opportunity to practice what he/she has learned in the theoretical introduction through a personal research project covering real linguistic data.

Teaching methods

Lectures + readings + practical works

Evaluation methods

The evaluation is three-fold :

• continuous assessment (exercices during TP and readings) (30 %)
  
• written examination (30 %)
  
• personal written essay (40 %)
  

In September, the evaluation is adapted as follows:

• written examination (50 %)
  
• personal written essay (50 %)
  

Generative artificial intelligence (AI) must be used responsibly and in accordance with the practices of academic and scientific integrity. Scientific integrity requires that sources be cited, and the use of AI must always be reported. The use of artificial intelligence for tasks where it is explicitly forbidden will be considered as cheating.

Other information

Support (available on Moodle) :

• slides;
• articles ou book chapters;
• additional exercices.

Bibliography

Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
Howell, D. (2008). Méthodes statistiques en sciences humaines, Paris, De Boeck Université.
Muller, Charles (1992). Initiation aux méthodes de la statistique linguistique, Champion.
Rasinger, S.M. (2008). Quantitative Research in Linguistics. New York, Continuum International Publishing Group

Faculty or entity

> ELAL