LIN 392 Analyzing Linguistic Data: Schedule

Courses‎ > ‎Analyzing linguistic data (graduate)‎ > ‎

LIN 392 Analyzing Linguistic Data: Schedule

Home

This schedule is subject to change.

Assignments are due at the end of their due date (midnight).

Readings can be done either before or after class; they are chosen to support the material covered in class.

Week 1

Aug 27: Introduction

Links:

Google trends (try a classic search: "turkey" vs. "diet")
Google n-gram viewer
LL breakfast experiment: passive voice
LL breakfast experiment: Analyzing real estate listings, and more of the same

Week 2

Sep 1: First steps in R
- Data: Word counts in inaugural speeches

Please install the R statistics package on your laptop before this lecture. I recommend RStudio. Please use the free version, not the commercial license.
Readings: ALD ch. 1

Sep 3: Continuing on the worksheet on First steps in R

Week 3

Sep 9: Descriptive statistics: central tendency and spread. Data visualization

Readings: ALD ch. 2 up to page 32
Readings: SE chapter 2
R worksheets: merge and aggregate
For computing the mode of a sample, see the R code snippets page

Sep 11: Data visualization, continued

Week 4

Sep 15: Invited talk: Statistics in practice

Sep 17: Invited talk: Statistics in practice

Week 5

Sep 22: Probability distributions. Samples and populations.

Readings: SE ch. 3, ALD ch. 3 pp. 44-63

Sep 24: Statistical tests: the basic principle; significance thresholds

Readings: SE ch. 4,5

Week 6

Sep 29: The t-test

R code: the t-test
Readings: SE ch. 6,7
Homework 2 due

Oct 1: The t-test continued

Also: R code: reading, preprocessing and counting text.
Readings: SE ch. 8, 9

Week 7

Oct 6: Statistical testing: chi-squared

Readings: SE ch. 10, 19

Oct 8: t-test exercises. Statistical testing: chi-squared continued. Comparing more than two data sets: ANOVA

Pitfalls of statistical analysis: The problem of multiple testing https://xkcd.com/882/
R code: ANOVA
Readings: SE ch. 11, 12
Homework 3 due
Data: Fisher word counts, Fisher telephone corpus, analyzed using LIWC (warning: big dataset!), meta-data for the Fisher telephone corpus

Week 8

Oct 13: Correlation

Readings: SE ch. 20, 261-70
R code: Correlation and linear regression
Discussion in class about your project ideas

Oct 15: Linear regression

Readings: SE rest of ch. 20
R code: linear regression
A linear regression tutorial
Project proposal due

Week 9

Oct 20: Linear regression in R

R code: more linear regression
Readings: ALD ch. 4 pp. 84 - 10; SE ch. 21

Oct 22: Multiple regression, and different types of predictors

SE ch. 21
How not to do a scientific study: "p-value hacking"

Week 10

Oct 27:Logistic regression
Oct 29: Logistic regression in R

Readings: ALD ch 6 pp 195-199, 202-203
R code: logistic regression
Homework 4 due

Week 11

Nov 3: Model comparison

Readings: ALD ch. 6 pp 174-188
R code: model comparison

Nov 5: Introduction to mixed-effects models

A mixed-effects regression tutorial with associated data
Progress report due

Week 12

Nov 10: Mixed-effects models in R

Readings: ALD ch. 7 pp 241-251

Nov 12: Practising regression

Homework 5 due
Readings: ALD ch. 7 pp. 278-284

Week 13

Nov 17: Clustering for data exploration

R code: clustering
Readings: ALD ch. 5 pp 138-148

Nov 19: Dimensionality reduction

Readings: ALD ch. 5 from beginning up to (and including) 5.1.4

Week 14

Nov 24: A brief look at topic modeling.
Also: pitfalls of statistical analyses

Topic modeling: a demo

Nov 26: Thanksgiving

Week 15

Dec 1: Project presentations

11:00 Lam
11:15 Fuchs
11:30 Schrubbe
11:45 Fingerhuth
12:00 Hyska

Dec 3: Project presentations

11:00 Lorenz
11:15 Sokhey
11:30 Griffiths
11:45 Callesano
12:00 Leslie

Final report due: Wednesday December 9, end of day

Comments

Katrin Erk's homepage	Search this site