LEADER 04223nam 22006015 450 001 9910300120703321 005 20200707031403.0 010 $a3-319-69830-3 024 7 $a10.1007/978-3-319-69830-4 035 $a(CKB)3840000000347663 035 $a(MiAaPQ)EBC5275419 035 $a(DE-He213)978-3-319-69830-4 035 $a(PPN)224639242 035 $a(EXLCZ)993840000000347663 100 $a20180207d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aMixed-Effects Regression Models in Linguistics /$fedited by Dirk Speelman, Kris Heylen, Dirk Geeraerts 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (146 pages) $cillustrations, graphs 225 1 $aQuantitative Methods in the Humanities and Social Sciences,$x2199-0956 311 $a3-319-69828-1 320 $aIncludes bibliographical references at the end of each chapters. 327 $aChapter 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 O?ered 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. 330 $aWhen data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses. 410 0$aQuantitative Methods in the Humanities and Social Sciences,$x2199-0956 606 $aStatistics 606 $aSemantics 606 $aSyntax 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aSemantics$3https://scigraph.springernature.com/ontologies/product-market-codes/N39000 606 $aSyntax$3https://scigraph.springernature.com/ontologies/product-market-codes/N45000 615 0$aStatistics. 615 0$aSemantics. 615 0$aSyntax. 615 14$aStatistics for Social Sciences, Humanities, Law. 615 24$aSemantics. 615 24$aSyntax. 676 $a410.21 702 $aSpeelman$b Dirk$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHeylen$b Kris$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGeeraerts$b Dirk$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910300120703321 996 $aMixed-Effects Regression Models in Linguistics$91563661 997 $aUNINA