Syllabus for Current Research in Linguistics

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A revised version of the syllabus is available.

  • 7.5 credits
  • Course code: 5LN139
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: General Linguistics G1F
  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2016-12-21
  • Established by: The Department Board
  • Applies from: week 03, 2011
  • Entry requirements: 30 credits in a language subject.
  • Responsible department: Department of Linguistics and Philology

Decisions and guidelines

The course is a freestanding course. It is also given within Linguistics B and C as a choosable course.

Learning outcomes

This course will equip students with the basic skills to evaluate and carry out their own research in linguistics. On completion of the course a passing student will be able to:
- formulate clear and practical research questions and critically evaluate the research designof published papers;
- account forthe differences between quantitative and qualitative research methods, and know how each kind of research is properly used;
- write simple computer programmes using the R language to analyse and process data;
- make informative and attractive data visualisations;
- use appropriate methods to cluster and classifydata, to measure similarity between linguistic variables, and to test causal hypotheses;
- follow best practice in sharing and archiving data.


In this course students will learn the theory and practice of carrying out empirical research on language. The theoretical part of the course will be research design: the formulation of clear and practical research questions and the critical evaluation of the research design of published papers. The focus will be on applications to sociolinguistic and typological research, although the methods are applicable to other linguistic (and non-linguistic) domains. The practical part of the course will include an introduction to programming in the statistical computer language R. During the computer labs students will learn all the stages of data analysis: loading data from outside sources, manipulating data into appropriate forms, visualising data, performing simple statistical tests, and sharing and archiving results.


Teaching consists of lectures, computer labs and project work. For computer labs and project work students are encouraged to work collaboratively on problems, but assessable work must be written up individually. No prior experience in computer programming is assumed.


The assessment has two parts: exercises carried out in class and a final project. To pass the course a passing grade in both parts is required.

Syllabus Revisions

Reading list

The reading list is missing. For further information, please contact the responsible department.