course title
Advanced psycholinguistic data analysis with R (taught by Reinhold Kliegl and Shravan Vasishth)
dates and location
Thursdays 11-13.
II.24.1.78/79 (Computerpool I)
what this course is about
This course will cover statistical data analysis using R at an advanced level.
The goal is to enable students to independently carry out data analysis at an advanced level.
prerequisites
Some familiarity with R is desirable. There will be a couple of sessions in the beginning of the course to get everyone on board that does not have any background with R.
grading
Grading will be based on completion of assignments (70%) and class participation (30%). Class participation refers to asking good questions in class.
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.
Students are expected to attend class regularly. If a
class is missed, the student is responsible for finding out what the
assignment was, what readings were assigned, and what material was
covered.
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 instructors during class are actively encouraged.
textbooks
We will use Gelman and Hill 2007, Baayen 2008, Faraway 2005, and Vasishth and Broe (online).
rough schedule
Please sign up on moodle to get the code, data, slides, etc.
None of this is in the public domain so please do not distribute anything beyond the class to anyone.
Since there are three types of students in the class (beginner, intermediate, advanced) we will tailor the course so that everyone gets something out of it. Initially RK will work through a full example of data analysis from raw data to linear models-based analysis. Then we will have a brief digression where we learn to use literate programming techniques to document and manage our data analyses (specifially Sweave and friends); SV will teach this over two sessions.
Depending on student interests the second part of the course, starting January 2010 will be the promised advanced content.
| date |
topic |
reading |
presenter |
slides/data/code |
| Oct 22 |
introduction
|
-
|
RK
|
moodle
|
| Oct 29 |
intro contd
|
-
|
RK
|
moodle
|
| Nov 5 |
intro contd
|
-
|
RK
|
moodle
|
| Nov 12 |
Contrast coding practice I
|
-
|
SV
|
moodle
|
| Nov 19 |
Contrast coding practice II
|
-
|
SV
|
moodle
|
| Nov 26 |
Foundations of stats I
|
-
|
SV
|
moodle
|
| Dec 3 |
Foundations of stats 2
|
book
|
SV
|
moodle
|
| Dec 10 |
ANOVA |
|
RK
|
moodle
|
| Dec 17 |
ANOVA |
|
RK
|
moodle
|
| Jan 7 |
Contrasts, LMs in Matrix form |
Handout
|
RK and SV
|
|
| Jan 14 |
Contrasts, LMMs intro
|
|
RK and SV
|
|
| Jan 21 |
LMMs continued
|
|
SV
|
|
| Jan 28 |
LMMs continued
|
|
RK
|
|
| Feb 4 |
Class exercises with LMMs
|
|
RK and SV
|
|
| Feb 11 |
LMMs: conclusion
|
|
RK
|
|