Introduction to Experimental Syntax Methods

This is a first course in experimental syntax. The goal is to teach students how to think about syntactic data like an experimentalist. The course will be a combination of nuts-and-bolts tutorials, high-level conceptual discussions, and hands-on experience. When the course is over, students should be well-prepared to design, deploy, and analyze their own acceptability judgment experiments, and to extend that knowledge to other experimental methods in linguistics and psycholinguistics. Students should also be well-prepared to engage in cutting-edge debates about what (formal) experimental evidence can tell us about syntactic theory.

This course will work best if you bring a laptop with you to the class so that you can work along with the slides and discussion.

We are going to use Excel and R extensively in this course, so you should have them installed on your computer. Here are some links:

• A spreadsheet program such as Excel or Calc. If you don't have one, you can download OpenOffice for free.
• The R programming language. I highly recommend using the R Studio interface for working with R.
• The following packages for R: tidyverse, lmerTest. To install these, simply open R Studio or the R console (whichever you chose), type the following command into the console window, and press enter: install.packages(c("tidyverse", "lmerTest")). The tidyverse package will install several packages simultaneously (tidyR, dplyR, ggplot2) that we will use throughout the course.

Schedule and Materials

Here is a link to the full set of lecture slides for the course: - last updated 12.14.18. These basically form a textbook (in slide format) for the design and analysis of acceptability judgment experiments (over 300 slides).

All assignments are due before the start of class the following week

class	topic	data files	scripts	assignments
1	Sections 1 & 2: Introduction and Conditions			exercise 1
2	Section 3: Items			exercise 2
3	Section 4: Ordering items	full example experiment		exercise 3 exercise 4
4	Section 5: Judgment tasks	Sprouse 2011		no exercise
5	Section 6: Recruiting participants		AMT templates	exercise 5
6	Section 7: Pre-processing	item keys raw AMT data long format no items long format with items long format no outliers	convert to long format v1 convert to long format v2 add items, conditions, factors add z-scores v1 add z-scores v2 putting all scripts together identify outliers	exercise 6 exercise 7
7	Section 8: Plotting	long format no outliers	distribution plots interaction plots means and medians normal distribution parameters and statistics	exercise 8 data for 8
8	Section 9: Building linear mixed-effects models	long format no outliers	linear mixed-effects models interaction plot with points subject and item differences
9	Section 10: Fisher, F, and p	long format no outliers	randomization tests bootstrap tests the F distribution
10	Section 11: Neyman-Pearson	long format no outliers	the alpha level multiple comparisons
11	Section 12: Bayesian statistics and Bayes factors		the monty hall problem
12	Validity and replicability of judgments		papers to download
13	Spillover?