Data Visualisation and Statistics for Language Sciences

7.5 credits

Syllabus, Master's level, 5LN150

Code
5LN150
Education cycle
Second cycle
Main field(s) of study and in-depth level
General Linguistics A1N
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Department Board, 5 March 2021
Responsible department
Department of Linguistics and Philology

Entry requirements

120 credits including 90 credits in linguistics or another language subject. Proficiency in English equivalent to the Swedish upper secondary course English 6.

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 design of published papers
  • account for the 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 hypotheses
  • follow best practice in sharing and archiving data.

Content

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.

Instruction

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.

Assessment

The course has two moments of examination:

1. continuous written tests that are conducted during lecture hours;

2. a final project, the exact date for submission is announced by the teacher at the start of the course. 

For the grade Pass (G), Pass is required in all moments. For the grade Pass with distinction (VG), Pass with distinction is required in moment 2 as well as Pass in moment 1.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator.

Other directives

The course may not be included in a degree together with the course Current Research in Linguistics (5LN139).

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