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Quantitative Methods in Linguistics

Course Description

An introduction to quantitative and statistical approaches for analyzing human language. Topics include fundamentals of quantitative and empirical research, descriptive and analytical statistics, hypothesis testing, data modeling and visualization. Data are drawn from a wide range of linguistic subfields.

Additional Requirements for Graduate Students:
Graduate students will be held to higher standards on research skills and review of primary research to demonstrate application of techniques learned in this class to their own work. Specifically, graduate students: a. Will present a primary research article in class. Each will select an article that uses quantitative methods studied in class, and they will place the article in context, review and explain its contents, critique the quantitative methods it employs. Graduate students will take questions from classmates and instructor regarding the article. b. Will present their final projects to the class, and must complete projects individually. (Undergraduatess will not present; w/instructor permission, they may work in groups). c. Will document their computer programs and scripts (written in R), and upload them to a repository for use by other students. This will include three graded parts: the program, a description of its structure, an example data set to test.

Athena Title

Quant Methods Linguistics

Prerequisite

LING 3060 or LING 3150 or LING 3150W or LING 3250

Semester Course Offered

Offered spring

Grading System

A - F (Traditional)

Student Learning Outcomes

  • Students will be able to organize and manipulate linguistic data.
  • Students will be able to generate statistically testable hypotheses driven from data.
  • Students will be able to integrate quantitative and statistical methods into linguistic research in various subfields of the discipline.
  • Students will develop skills with R, RStudio, and R packages.
  • Students will be able to technically and practically interpret statistical.
  • Students will be able to apply statistical techniques like regression or ANOVAs to test hypotheses.

Topical Outline

  • 1. Introduction to quantitative methods a. Quantitative approaches in linguistics: a brief survey b. Why test a hypothesis? c. Introduction to the statistical analysis environment (software) d. Graphic data exploration e. Data types: continuous, categorical, nominal
  • 2. Descriptive analysis a. Capturing central tendencies b. Probability distributions, measures of dispersion c. Normalizing data: how and why d. How much data is enough?
  • 3. Basic statistical methods a. Testing for the mean, testing the difference between data sets b. Comparing continuous and categorical variables c. Analysis of variance d. Confidence intervals
  • 4. Analytical statistics a. Linear regression b. Mixed-effects models c. Logistic regression d. Interactions e. Evaluating model fit
  • 5. Clustering and classification a. Cladistics b. Classification trees c. Multidimensional scaling

Syllabus

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