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

By Bodo Winter. arXiv.org, 2013

633 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, General Social Sciences, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/60b203fec8d80f688aa864725dac9aa53e6eefbb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Robert A. I. Davies
2. Zhicheng Liu
3. Jeffrey Heer
4. Lotte Meteyard
5. M. Loon

In General Social Sciences

1. Robert A. I. Davies
2. Zhicheng Liu
3. Jeffrey Heer
4. Lotte Meteyard
5. M. Loon

In Statistics and Probability

1. Robert A. I. Davies
2. Zhicheng Liu
3. Jeffrey Heer
4. Lotte Meteyard
5. M. Loon

Citations in the corpus

• Best practice guidance for linear mixed-effects models in psychological science (2019, methodology): “…2016; Cunnings, 2012; Field, Miles & Field, 2009; Field & Wright, 2011; Jaeger, 2008; Kliegl, 2014; Magezi, 2015; Murayama, Sakaki, Yan, & Smith, 2014; Rasbah et al, 2000; Rabe-Hesketh & Skrondal, 2012; Schluter, 2015; Th. Gries, 2015; Tremblay & Newman, 2015; West & Galecki, 2011; Winter, 2013).”
• Analyzing dynamic phonetic data using generalized additive mixed modeling: A tutorial focusing on articulatory differences between L1 and L2 speakers of English (2018, neutral): “For more information about the merits about mixed-effects regression, we refer the interested reader to Baayen et al. (2008), Baayen (2008), Winter (2013), and Winter and Wieling (2016).”
• The Effects of Interactive Latency on Exploratory Visual Analysis (2014, methodology): “The common practice for assessing significance within mixed effects models is to use likelihood-ratio tests [43]: we build a full model (with the fixed effect in question) and a reduced model (without the fixed effect in question), and compare these models to obtain p-values.”
• Mixed effects models (2013, background): “Winter (2013) explains random effects as something that is usually nonsystematic and unpredictable, hence random influence on the data.”