Current Statistical Practice in Language Science

Catalog description

Our primary goal in this course is to explore how to analyze and interpret quantitative data in language science. Part of this goal will be to gain familiarity and proficiency with a range of quantitative techniques common in language science. Reflecting trends in the field, linear and logistic mixed effects regression will be a major focus in addition to more well-known (e.g. ANOVA, multiple regression, chi-square) techniques. We will also spend some time exploring other methods such as multidimensional scaling, generalized additive modeling, and conditional inference trees, as well as more specialized techniques (e.g. drift-diffusion modeling). A more important goal is to learn to think critically about quantitative data and how we can learn from it. This includes a critical view of quantitative research in general, questions of measurement, the many decisions involved in analytic strategy, model structure and interpretation, and the ability to extend students' knowledge to new techniques independently.