SPR 612 Statistics for Language Research

Programme

PhD programme at the Faculty of Humanities and education

Prerequisites

Students must be admitted to a relevant PhD programme.

Recommended previous knowledge

Basic knowledge in empirical research on language.

ECTS credits

5ECTS for attending all activities and handing in all specified requirements
3ECTS for attending two days and handing in the respective requirements (the amount of work will be alike for each day)

Learning Outcomes

After completing this course, students will be able to understand and evaluate statistical reports in scientific publications, investigate what kind of statistical methods are suitable for a certain data set, and analyse data collected for a linguistic research question.

Contents

The aim of this course is to introduce statistical analysis of quantitative data in the field linguistics. The course will consist of an introduction to statistical analysis (Day 1), a presentation of one's own work (Day 2), and a workshop in R (Day 3).

Teaching methods

The course will include lectures, student presentations with discussion, group work, and practical exercises. The first day will be held in hybrid mode (students can participate online or on Campus). However, it is highly recommended to attend in person. The other two days will be conducted solely in person.

Examination requirements

The course will award 5 ECTS to participants who participate on all three days and 3 ECTS to participants who participate on two elected days.

Day 1 (hybrid mode): Students will need to

• read the required literature before and after the course,
• complete all quizzes and tasks before and after the course,
• participate in all lectures and exercises.

Day 2 (on Campus): Students will need to

• give a presentation in class,
• send in the required preparations (2-3hours),
• participate in all lectures and exercises.

Day 3 (on Campus): Students will need to

• complete the required technical preparations (0.5-1hour),
• participate in all lectures and exercises,
• complete a project in R.

Assessment methods and criteria

The participants are required to:

• submit required preparations in advance
• participate actively in lectures and exercises
• complete all quizzes and tasks before and after the course
• give a presentation in class
• complete the required technical preparations
• complete a project in R

All requirements must be approved as passed for a student to pass the course.

Offered as a free-standing course

Yes, subject to availability