TY - BOOK ID - 32842150 TI - Mixed-Effects regression models in linguistics AU - Speelman, Dirk. AU - Heylen, Kris. AU - Geeraerts, Dirk. PY - 2018 SN - 3319698303 3319698281 9783319698281 9783319698304 PB - Cham Springer DB - UniCat KW - Linguistics KW - Regression analysis. KW - Statistical methods. KW - Statistics. KW - Semantics. KW - Syntax. KW - Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. KW - Formal semantics KW - Semasiology KW - Semiology (Semantics) KW - Comparative linguistics KW - Information theory KW - Language and languages KW - Lexicology KW - Meaning (Psychology) KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Mathematics KW - Econometrics KW - Analysis, Regression KW - Linear regression KW - Regression modeling KW - Multivariate analysis KW - Structural equation modeling KW - Linguistics, Statistical KW - Statistical linguistics KW - Mathematical linguistics KW - Grammar, Comparative and general. KW - Statistics for Social Sciences, Humanities, Law. KW - Comparative grammar KW - Grammar KW - Grammar, Philosophical KW - Grammar, Universal KW - Philosophical grammar KW - Philology KW - Grammar, Comparative KW - Statistics . KW - Grammar, Comparative and general Syntax KW - Syntax UR - https://www.unicat.be/uniCat?func=search&query=sysid:32842150 AB - This book discusses mixed models at a conceptual level, but also presents the practical usage of the technique. When data consists of grouped obervations or clusters, and there is a risk that measurements within the same groups 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. ER -