This page serves as the syllabus for this course.
Official course textbooks:
R.H. Baayen (2008): Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. Cambridge University Press.
P. R. Hinton (2004): Statistics Explained: A Guide for Social Science Students. Psychology Press; 3rd edition.
We will also make use of other readings, which will be made available on the Schedule page.
Assignments will be updated on the Assignments page. A tentative schedule for the entire semester is posted on the Schedule page. Readings and exercises may change up a week in advance of their due dates. There is an end-of-term project for the course, where students will be expected to choose a dataset that they intend to analyze. Details on the requirements for the project are given on the Assignments page.
Many research topics in linguistics can benefit from sophisticated statistical analysis of language datasets. This course will introduce fundamental concepts that will enable students to formulate quantitatively-oriented research questions and answer them with appropriate visualization, modeling and testing. Students will learn these techniques, apply them to data sets in class, and generalize them to a dataset of their own choice.
We use the R programming language, which allows much more flexible and customizable ways of performing such exploration and analysis, compared to statistical packages based on point-and-click interfaces (like SPSS). It also forms a strong basis for using more complex modeling techniques than are covered in this course—including writing one's one code to do so.
This course provides hands-on introduction to statistics for language, using the R programming language. Using data from existing linguistic studies, we will study the following topics:
For information on the homework assignments or on project requirements, see the Assignments page.
If you turn in your assignment late, expect points to be
deducted. Extensions will be
considered on a case-by-case basis.
If you anticipate that you will need an extension for some assignment, let me know in advance.
By default, 5 points (out of 100) will be deducted for lateness, plus an additional 1 point for every 24-hour period beyond 2 that the assignment is late. For example, an assignment due at 2pm on Tuesday will have 5 points deducted if it is turned in late but before 2pm on Thursday. It will have 6 points deducted if it is turned in by 2pm Friday, etc.
Even if you are late for some assignment, you should definitely turn it
in, and you will get some credit for your work, even though some points
may be deducted. But it is crucial for your learning progress that you
do all the coursework.
You are encouraged to discuss assignments with classmates. But all written work must be your own. Students caught cheating will automatically fail the course. If in doubt, ask the instructor.
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. Please contact the Division of Diversity and Community Engagement, Services for Students with Disabilities, 512-471-6259, http://www.utexas.edu/diversity/ddce/ssd/
By UT Austin policy, you must notify me of your pending absence at least fourteen days prior to the date of observance of a religious holy day. If you must miss a class, an examination, a work assignment, or a project in order to observe a religious holy day, you will be given an opportunity to complete the missed work within a reasonable time after the absence.