Mixed-Effects Regression Models in Linguistics

1. Front Matter

   Pages i-vii

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2. Introduction

   • Dirk Speelman, Kris Heylen, Dirk Geeraerts

   Pages 1-10

3. Mixed Models with Emphasis on Large Data Sets

   • Geert Verbeke, Geert Molenberghs, Steffen Fieuws, Samuel Iddi

   Pages 11-28

4. The L2 Impact on Learning L3 Dutch: The L2 Distance Effect

   • Job Schepens, Frans van der Slik, Roeland van Hout

   Pages 29-47

5. Autocorrelated Errors in Experimental Data in the Language Sciences: Some Solutions Offered by Generalized Additive Mixed Models

   • R. Harald Baayen, Jacolien van Rij, Cecile de Cat, Simon Wood

   Pages 49-69

6. Border Effects Among Catalan Dialects

   • Martijn Wieling, Esteve Valls, R. Harald Baayen, John Nerbonne

   Pages 71-97

7. Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion

   • Danielle Barth, Vsevolod Kapatsinski

   Pages 99-116

8. (Non)metonymic Expressions for government in Chinese: A Mixed-Effects Logistic Regression Analysis

   • Weiwei Zhang, Dirk Geeraerts, Dirk Speelman

   Pages 117-146

When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. 

In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.

• effects models
• generalized linear mixed models
• linguistics
• mixed models
• regression
• semantics

“I assume that the intended primary audience for this book is those scientists working linguistic domain. I would safely conclude that that book is also useful for those who are interested in, collecting, and analyzing such data in other fields of applications.” (S. Ejaz Ahmed, Technometrics, Vol. 60 (3), 2018)​

• Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

  Dirk Speelman, Kris Heylen, Dirk Geeraerts

Dirk Speelman is associate professor at the department of linguistics at the KU Leuven. Dirk's main research interest lies in the fields of corpus linguistics, computational lexicology and variational linguistics in general. Much of his work focuses on methodology and on the application of statistical and other quantitative methods to the study of language. 

Kris Heylen is a research fellow at the research group Quantitative Lexicology and Variational Linguistics at the University of Leuven (KU Leuven, Belgium) and research fellow at the Institute for the Dutch Language (INT, Leiden, The Netherlands). He specialises in the corpus-based, statistical modelling of lexical semantics and lexical variation. 

Dirk Geeraerts is professor of linguistics at the University of Leuven, where founded the research unit Quantitative Lexicology and Variational Linguistics. His main research interests involve the overlapping fields of lexical semantics and lexicology, with a specific descriptive interest in social variation, a strong methodological commitment to corpus analysis, and a theoretical background in Cognitive Linguistics.

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