Mixed-Effects Regression Models in Linguistics

Nonfiction, Social & Cultural Studies, Social Science, Statistics, Reference & Language, Language Arts, Linguistics
Cover of the book Mixed-Effects Regression Models in Linguistics by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783319698304
Publisher: Springer International Publishing Publication: February 7, 2018
Imprint: Springer Language: English
Author:
ISBN: 9783319698304
Publisher: Springer International Publishing
Publication: February 7, 2018
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Renewable Energy in the UK by
Cover of the book Sweden: From Neutrality to International Solidarity by
Cover of the book Doing Business in ASEAN Markets by
Cover of the book Logistics and Supply Chain Innovation by
Cover of the book Finite Approximations in Discrete-Time Stochastic Control by
Cover of the book Computer Vision in Sports by
Cover of the book Developments in Medical Image Processing and Computational Vision by
Cover of the book Imaging and Diagnosis in Pediatric Brain Tumor Studies by
Cover of the book Biomaterials in Regenerative Medicine and the Immune System by
Cover of the book Geographies of Disruption by
Cover of the book Contemporary Italian Narrative and 1970s Terrorism by
Cover of the book Towards Robust Algebraic Multigrid Methods for Nonsymmetric Problems by
Cover of the book Mobility and Ancient Society in Asia and the Americas by
Cover of the book The Obesity Epidemic by
Cover of the book Air Pollution Modeling and its Application XXV by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy