Note: This course is only open to students in the BA program in Linguistics. You are required to take Wolf Schwarz' course on Introductory Statistics. Because of computer space restrictions, only 24 students can take part in this course.

Final grades

Homework scores predict the final outcome 



 732895  3.7
 741956  4.0
 738944  1.7
 741972  3.7
 730395  5.0
 738193  5.0
 738270  1.3
 738020  1.3
 739494  3.7
 752005  3.7
 738747  3.3
 738120  2.7
 738838  1.3
 741970  1.0
 738672  2.3
 738528  3.3
 738254  2.0
 739050  3.0
 739122  5.0
 741351  5.0
 739243  5.0




Summary: what this course will cover

This course will teach you the following practical skills necessary to become a linguist (or indeed anything else):
  • What constitutes data in linguistics?
  • What methods can be used to gather data?
  • We will also learn the answer to the question: What do you do with the data that you end up gathering? Some programming will have to be learnt, but really very little.
  • Finally, we will learn to write down the results of an empirical data-gathering exercise.


    • Course information

  • Time: Fridays 09:00 - 11:00AM
  • Teaching period in summer semester: 14.04.2006-20.07.2007
  • Place: Golm campus, Haus 24, Room 1.54 and Room 1.78/79 (CIP Pool)
  • Office hours: Friday 11-1PM
  • Readings: We will use Keith Johnson's book (available online), Quantitative Methods in Linguistics


    • Schedule

    The schedule will show the slides and assignments as they come up. That's why only the dates are shown at the moment.
  • April 18:
    Location: Haus 24, 1.54 (later on during the class we will move to the CIP Pool)
    Introductory lecture (handout to be provided), and short R tutorial
    Handout: here
    Code: here
    Data: here
    Homework: Chapter 1 of Johnson's online textbook (see readings above).
  • April 25: NO CLASS
  • May 2: Location: Haus 24, the CIP Pool
    Here is what we will do in this class:
    1. Very small quiz on chapter 1 of textbook (5 minutes maximum). The questions I will ask are the following:
    a. Define mean, median and mode.
    b. Define standard deviation.
    2. Complete working through the code in the handout from last week
    Download the lecture notes for today.
    Download the code accompanying the lecture notes for today.
    3. Download the class exercises.
    Download the code for the class exercises.
    4. The beauty and the beasts: Do good-looking professors get higher teaching evaluation scores? Background reading: see this website.
    Download the beauty data.
  • May 9: Code for today.
  • May 16: Code for today.
  • May 23: Code for today.
  • May 30:
  • June 6: NO CLASS
  • June 13: We will meet first in the lecture room and then go to the lab Code for today.
  • June 20: We will again meet in the lecture room first and then go to the lab Code for today.
  • June 27: Code and graphic for today.
  • July 4
  • July 11
  • July 18


    • Grading

    In-class quizzes 10%, Homework 30%, Final 60%. 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 1st, I will adjust their scores so that 1st semesters are not at a disadvantage (this holds only if the advanced students outperform the 1st 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 homework assignment was, what readings were assigned, and what material was covered.

    Homework submission dates are strict (usually beginning of class the following week). Late homework will not receive any credit.


    Conduct in the classroom

  • Please do not engage in private conversations during class.
  • Please do not read emails or play games on the computer while in class.
  • Please do not walk into class after it starts (09:15AM is the deadline to be ready for class).
  • All cell phones must be switched off (except by permission from me).
  • Questions to me during class are actively encouraged.


    • Evaluation of the instructor

  • Instructor's name: Shravan Vasishth.
  • Anonymous feedback (especially complaints about the course) is welcome: Click here for form


    • Official language of the course

    I will teach in English, but questions in German are welcome. Homework etc can also be submitted in German.