rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Mixed-Effects Regression Models in Linguistics
  name:
    name part: Speelman, Dirk.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Heylen, Kris.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Geeraerts, Dirk.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VII, 146 p. online resource.
  abstract: 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.
  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.
  note: edited by Dirk Speelman, Kris Heylen, Dirk Geeraerts.
  subject:
    topic: Statistics
  subject:
    topic: Semantics
  subject:
    topic: Syntax
  subject:
    topic: Statistics for Social Sciences, Humanities, Law
  subject:
    topic: Semantics
  subject:
    topic: Syntax
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Quantitative Methods in the Humanities and Social Sciences
  identifier: 9783319698304
  identifier: https://doi.org/10.1007/978-3-319-69830-4
  record info:
    record creation date: 180207
    record change date: 20191026091410.0
    record identifier: 978-3-319-69830-4