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Mixed-effects regression models in l...

Geeraerts, Dirk.

Mixed-effects regression models in linguistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mixed-effects regression models in linguisticsedited by Dirk Speelman, Kris Heylen, Dirk Geeraerts.
other author:	Speelman, Dirk.
Published:	Cham :Springer International Publishing :2018.
Description:	vii, 146 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	LinguisticsStatistical methods.
Online resource:	http://dx.doi.org/10.1007/978-3-319-69830-4
ISBN:	9783319698304$q(electronic bk.)

Mixed-effects regression models in linguistics
Mixed-effects regression models in linguistics[electronic resource] /edited by Dirk Speelman, Kris Heylen, Dirk Geeraerts. - Cham :Springer International Publishing :2018. - vii, 146 p. :ill. (some col.), digital ;24 cm. - Quantitative methods in the humanities and social sciences,2199-0956. - Quantitative methods in the humanities and social sciences..

Chapter 1. Introduction -- Chapter 2. Mixed Models with Emphasis on Large Data Sets -- Chapter 3. The L2 Impact on Learning L3 Dutch: The L2 Distance Effect Job -- Chapter 4. Autocorrelated Errors in Experimental Data in the Language Sciences: Some Solutions Offered by Generalized Additive Mixed Models -- Chapter 5. Border Effects Among Catalan Dialects -- Chapter 6. Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion -- Chapter 7. (Non)metonymic Expressions for Government in Chinese: A Mixed-Effects Logistic Regression Analysis.

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.

ISBN: 9783319698304$q(electronic bk.)

Standard No.: 10.1007/978-3-319-69830-4doiSubjects--Topical Terms:

217133
Linguistics
--Statistical methods.

LC Class. No.: P138.5

Dewey Class. No.: 410.151

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505 0 $a Chapter 1. Introduction -- Chapter 2. Mixed Models with Emphasis on Large Data Sets -- Chapter 3. The L2 Impact on Learning L3 Dutch: The L2 Distance Effect Job -- Chapter 4. Autocorrelated Errors in Experimental Data in the Language Sciences: Some Solutions Offered by Generalized Additive Mixed Models -- Chapter 5. Border Effects Among Catalan Dialects -- Chapter 6. Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion -- Chapter 7. (Non)metonymic Expressions for Government in Chinese: A Mixed-Effects Logistic Regression Analysis.
520 $a 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.
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