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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.

Détails
Auteur
Dirk  Geeraerts , Kris  Heylen , Dirk  Speelman
Marque
Springer
Collection
Quantitative Methods in the Humanities and Social Sciences
Parution
2018-02-07
Pages
146 pages
EAN papier
9783319698281
Langue
Anglais
Informations ebook
EAN PDF
9783319698304
Prix
126,59 €
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Nb pages copiables 1
Nb pages imprimables 14
Taille du fichier 3287 Ko
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