Linear models and linear mixed effects models in R with linguistic applications

TL;DR

This paper introduces mixed effects modeling in R for linguistic data analysis, covering linear models, assumptions, and multilevel modeling with an example on voice pitch analysis.

Contribution

It provides a conceptual overview of mixed effects models in R tailored for linguistic applications, emphasizing random effects and model testing.

Findings

Explains linear and mixed effects models in R for linguistics

Includes an example analysis of voice pitch data

Discusses model assumptions and testing methods

Abstract

This text is a conceptual introduction to mixed effects modeling with linguistic applications, using the R programming environment. The reader is introduced to linear modeling and assumptions, as well as to mixed effects/multilevel modeling, including a discussion of random intercepts, random slopes and likelihood ratio tests. The example used throughout the text focuses on the phonetic analysis of voice pitch data.