Mixed-Effects Regression Models in Linguistics

Illustrates the diversity of applications of mixed models now found in linguistics and applicable for other disciplines in the humanities and social sciences
Uses unique, hands-on approach to demonstrate statistical method
Significant, current linguistic research projects are used as case studies to teach particular applications of mixed effects models

Auteur

Dirk Speelman is associate professor at the department of linguistics at the KU Leuven. Dirk's main research interest lies in the fields of corpus linguistics, computational lexicology and variational linguistics in general. Much of his work focuses on methodology and on the application of statistical and other quantitative methods to the study of language.

Kris Heylen is a research fellow at the research group Quantitative Lexicology and Variational Linguistics at the University of Leuven (KU Leuven, Belgium) and research fellow at the Institute for the Dutch Language (INT, Leiden, The Netherlands). He specialises in the corpus-based, statistical modelling of lexical semantics and lexical variation.

Dirk Geeraerts is professor of linguistics at the University of Leuven, where founded the research unit Quantitative Lexicology and Variational Linguistics. His main research interests involve the overlapping fields of lexical semantics and lexicology, with a specific descriptive interest in social variation, a strong methodological commitment to corpus analysis, and a theoretical background in Cognitive Linguistics.

Contenu
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 Oered 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.