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Current Research in Linguistics, 7.5 c

Course code:5LN139, Report code:07709, 50%, DAG, NML
week: 14 - 23 Semester: Spring 2019 (2019-04-01 - 2019-06-09)

This course is part of a joint section.

Course registration

Registration for this course is done via roll call.

Time of registration: 2019-04-04 time 14.15 - 16.00

Place of registration: Turing

Information for admitted students

Registration takes place at the first lecture or seminar. Students who are conditionally admitted need to prove they are eligible to the course before they can be registered.
If you are unable to participate in the first class you need to contact the responsible teacher (se contact information in the teacher list to the left).

Information for reserves

Students who have applied late will be automatically placed on queue, and will be contacted by the department by email (from info@lingfil.uu.se) if they can be offered a place on the course. The email will be sent to the address given at the time of application. Please ensure that the email address is valid.

Contact information

If you have any questions about registration, please contact:
Email: info@lingfil.uu.se

Information about student accounts

To take this course you must have a student account. As an admitted you can activate your student account via www.uu.se/konto.

Collaboration information

Linguistic Research and Research Method

TEACHER

Instructor: Marc Tang

Course coordinator: Michael Dunn

Please contact Marc Tang if you have any questions about the 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:

  • understand 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, visualize, and process data
  • use appropriate methods to cluster data, to measure similarity between linguistic variables, and to test causal hypotheses
  • formulate clear and practical research questions of your own and critically evaluate the research design of published papers
  • follow best practice in sharing and archiving data

 

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. 

The teaching method will be interactive and involve group work. Working groups will provide overviews and questions about the learned content along the course.

No prior experience in computer programming is assumed. Students are encouraged to bring their own computer to use for exercises during the class. The computers of the lab may also be used upon request.

 

ASSESSMENT

The assessment has two parts: exercises carried out in class (overviews and questions about learned knowledge), and a final project . To pass the course a passing grade in both parts is required.

In the final project (1000-1500 words), the student should formulate a research question based on the dataset provided by the instructor and use R to visualize and analyze the data. The final project should include text description of each code along with its output. The final project should be submitted via Student Portal before Friday 14.6.2019 at 23.00. The grades used are U, G, and VG. 

Please read the rules about cheating and plagiarism. Delayed course work without a permit will only receive grades U or G, without written feedback.

 

LESSON PLAN (EACH SESSION IS 1H45 MINUTES, INCLUDING BREAKS)

W14 April 4, Thursday kl. 14-16.  Quantitative and qualitative research
W15 April 11, Thursday kl. 14-16. Introduction to R and R studio (R)
W16 April 18, Thursday kl. 14-16. Basic quantitative/statistical concepts (R)
W17 April 25, Thursday kl. 14-16. Data structure and plotting (R - visualise)
W18 May 2, Thursday kl. 14-16. Data wrangling (R - visualise/analyse)
W19 May 9, Thursday kl. 14-16. Data visualisation and exploration (R - analyse)
W20 May 16, Thursday kl. 14-16. Experimental design for linguistics (Research Question)
W21 May 23, Thursday kl. 14-16. Evaluating a published paper (Research Question)
W22 May 29. Wednesday kl. 10-12. Working with similarity measures (R - analyse)
W23 June 5, Wednesday kl. 10-12. Reproducible and publishable research (Archiving) 

*All course are carried out at the Turing lab (9-2042)

*W21 will also include a discussion of potential topics for the final project

 

RESOURCES

(partial list)

Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K. Teal. 2017. “Good Enough Practices in Scientific Computing.” PLOS Computational Biology13 (6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510.

Levshina, Natalia. 2015. How to do Linguistics with R: Data exploration and statistical analysis.John Benjamins.    

Materials from the Software Carpentry consortium, incl.

  • Programming with R syllabus: http://swcarpentry.github.io/r-novice-inflammation/ 
  • R for reproducible research syllabus: http://swcarpentry.github.io/r-novice-gapminder/guide/
  • Version control syllabus: http://swcarpentry.github.io/git-novice/