Mixed-Effects Regression Models in Linguistics

Description

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. Front Matter ....Pages i-vii Introduction (Dirk Speelman, Kris Heylen, Dirk Geeraerts)....Pages 1-10 Mixed Models with Emphasis on Large Data Sets (Geert Verbeke, Geert Molenberghs, Steffen Fieuws, Samuel Iddi)....Pages 11-28 The L2 Impact on Learning L3 Dutch: The L2 Distance Effect (Job Schepens, Frans van der Slik, Roeland van Hout)....Pages 29-47 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 Border Effects Among Catalan Dialects (Martijn Wieling, Esteve Valls, R. Harald Baayen, John Nerbonne)....Pages 71-97 Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion (Danielle Barth, Vsevolod Kapatsinski)....Pages 99-116 (Non)metonymic Expressions for government in Chinese: A Mixed-Effects Logistic Regression Analysis (Weiwei Zhang, Dirk Geeraerts, Dirk Speelman)....Pages 117-146