course title
Practical data analysis using R

dates and location
Tuesdays 1100-1300, Golm campus, Haus 24, CIP Pool

what this course is about
This is a hands-on course about data analysis for psycholinguists and linguists doing or planning to do experiments. We will be using this book. You can buy a physical copy (recommended) over (20 Euros). Also download the package languageR on your own machine from here.

prerequisites
I will presuppose that you have taken Wolf Schwarz' introduction to statistics, and my course on empirical methods in linguistics. If you do not have this background, you should have read and worked through this book before you come to class.
Familiarity with programming will help but is not necessary. Fearlessness is essential. We will be using R, which is freely downloadable from here.

tutor: Tobias Guenther
Tobias Guenther, who is also a student in the class, will provide regular tutorials (in German) in addition to the lectures. Please visit his web page for details.

grading
Grading will be based on in-class assignments, homework assignments, and a final project. There will be no in-class final examination.
In class assignments have 30% weight, homework assignments have 30% weight and final project has 40% weight in the final scoring.
Final scores will be based on the following mapping described in the Studienordnung: 95-100%=1,0 (A);90-94=1,3 (A-);85-89=1,7 (B+);80-84=2,0 (B);75-79=2,3 (B-);70-74=2,7 (C+);65-69=3,0 (C);60-64=3,3 (C-);55-59=3,7 (D+);50-54=4,0 (D);45-49=5,0 (F). If a student's score falls between the cracks, it will be treated as falling in the higher bin. Also, if a student is in a higher semester than the regular BA students, I will adjust their scores so that lower semesters are not at a disadvantage (this holds only if the advanced students outperform the lowerx semesters).
Here is an excerpt from the Studienordnung on what these major categories are supposed to mean:
  • 1 = sehr gut (eine hervorragende Leistung)
  • 2 = gut (eine Leistung, die erheblich ueber den durchschnittlichen Anforderungen liegt)
  • 3 = befriedigend (eine Leistung, die durchschnittlichen Anforderungen entspricht)
  • 4 = ausreichend (eine Leistung, die trotz ihrer Maengel noch den Anforderungen genuegt)
  • 5 = nicht ausreichend (eine Leistung, die wegen erheblicher Maengel den Anforderungen nicht genuegt)
    • Students are expected to attend class regularly. If a student misses a class, the student is responsible for finding out what the assignment was, what readings were assigned, and what material was covered.
    Note: If more than three homework submissions are missed, the student fails the course.

    conduct in the classroom
  • Please do not engage in private conversations during class.
  • All cell phones must be switched off (except by permission from me).
  • Please do not walk into class after it starts (11:15 is the deadline to be ready for class).
  • Questions to me during class are actively encouraged.


    • evaluation of the instructor
    Anonymous feedback (especially complaints about the course) is welcome: Click here for form

    schedule
    There is no rigid schedule. We will follow the book at a pace that everyone can handle.

    date topic downloads homework solution
    Apr 21 Basics of R chapter1.R Read chapter 1 -
    Apr 28 Plotting data (basics) all files as archive, Sweave how-to, Sweave slides by Peng Exercises 1,2, 4 in the Workbook section of chapter 2
    May 5 Distributions chapter3.R,chapter3.pdf,chapter3.Rnw. Read chapter 3
    May 12 T-tests and stuff chapter4.R, chapter4.pdf, chapter4.Rnw, kid iq Kid IQ questions
    May 19 Linear models lecture5.R, beauty data, babies data, handout
    May 26 class cancelled due to illness
    June 2 Linear models, SD vs SE, 95% CIs lecture6.R, einander.R, plotcoefs.R babies data solution
    June 9
    June 16
    June 23 Mixed-effects models lmertutorialRclass.R, mathachieve.txt, lmertutorial.pdf
    June 30 Mixed-effects models Get data from moodle
    July 7 Mixed-effects models
    July 14 Exercises