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Mixed-Effects Regression Models in Linguistics

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-s...

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Bibliographic Details
Other Authors: Speelman, Dirk (Editor), Heylen, Kris (Editor), Geeraerts, Dirk (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2018, 2018
Edition:1st ed. 2018
Series:Quantitative Methods in the Humanities and Social Sciences
Subjects:
Syntax
Social Sciences / Statistical Methods
Semiotics
Grammar, Comparative And General / Syntax
Statistics In Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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

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