LING280: Experimental methods in linguistics
Matt Wagers, instructor
Winter, 2013

Meets Tu/Th 2pm - 3:45,
in Stevenson 213

course resources

required text

The Foundations of Statistics: A Simulation-based Approach by Vasishth & Broe, Springer, 2010

supplementary

Handbook of Biological Statistics By John H. McDonald (UDel), lucid and accessible discussions

Introduction to Probability and Statistics using R By G. Jay Kerns (Youngstown State), very helpful integration of statistical concepts with R

There is a library of books about experimental design, statistics for linguistics research, R, etc. in 232 Stevenson.

R resources

CRAN contributed R tutorials

Short's R reference card

Quick-R Web site

Google's R Style Guide

handouts

Transcripts, notes, etc.
  1. Meeting 1
    (R, sample statistics, CDF, etc.)
  2. Meetings 2 & 3
    (Binomial distribution) NatesGame.R
  3. Meeting 4
    (Sampling distribution; Central Limit Theorem)
  4. Meeting 5
    (Normal distribution, confidence intervals)
  5. Meeting 6 - Exp. design
  6. Meeting 7-8
    (Two-sample t-tests, ANOVA I)
  7. Meeting 9
    (ANOVA II)
  8. Meeting 8: RC Raising diagnostics
  9. Meeting 11: Linear models, intro
  10. Useful class functions
  11. Tools for simple survey construction
Lightly edited after class; send errors to M. Wagers

Assignments
Due date in parentheses.

  1. Problem Set 1 (Jan 15) (solution)
  2. Problem Set 2 (Jan 22)
  3. Problem Set 3 (Feb 1)
  4. Human subjects training (Feb 15)
  5. In-class A (No date)

 

course goals

  1. To introduce a set of principles for reasoning under uncertainty, i.e., inference via statistical model building and comparison;
  2. To apply those principles in the design and analysis of linguistics experiments.

To accomplish these objectives, we will learn to conduct two kinds of experiments: an acceptability study and a reaction time study. These two basic studies cover a wide range of issues and concerns and are of primary importance to linguistics since they allow the investigator to establish distributional facts about a language; to map the time-course of language processes; and, more generally, to discover patterns of complexity in linguistic knowledge.

This course is not a laundry-list introduction to a collection of tests and designs. It is about learning to build up your analysis from basic principles; becoming more adept consumers of information from the scientific and statistical literature; and thinking hard about how empirical observation and theory development feed one another.

meetings

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