arXiv (Cornell University)· 2013· Preprint
Linear models and linear mixed effects models in R with linguistic applications
- 481citations
- 2013year
Short summary
This paper introduces linear and linear mixed effects models in R for linguistic analysis, focusing on phonetic data.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces linear modeling and its assumptions.
- Explains mixed effects/multilevel modeling concepts like random intercepts and slopes.
- Demonstrates application using phonetic analysis of voice pitch data in R.
- Discusses likelihood ratio tests for model comparison.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. arXiv (Cornell University), 2013 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences