{"title":"How to analyze linguistic change using mixed models, Growth Curve Analysis and Generalized Additive Modeling","authors":"Bodo Winter, Martijn B. Wieling","doi":"10.1093/JOLE/LZV003","DOIUrl":null,"url":null,"abstract":"When doing empirical studies in the field of language evolution, change over time is an inherent dimension. This tutorial introduces readers to mixed models, Growth Curve Analysis (GCA) and Generalized Additive Models (GAMs). These approaches are ideal for analyzing nonlinear change over time where there are nested dependencies, such as time points within dyad (in repeated interaction experiments) or time points within chain (in iterated learning experiments). In addition, the tutorial gives recommendations for choices about model fitting. Annotated scripts in the online [Supplementary Data][1] provide the reader with R code to serve as a springboard for the reader’s own analyses. [1]: http://jole.oxfordjournals.org/lookup/suppl/doi:10.1093/jole/lzv003/-/DC1","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZV003","citationCount":"104","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Language Evolution","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/JOLE/LZV003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
引用次数: 104
Abstract
When doing empirical studies in the field of language evolution, change over time is an inherent dimension. This tutorial introduces readers to mixed models, Growth Curve Analysis (GCA) and Generalized Additive Models (GAMs). These approaches are ideal for analyzing nonlinear change over time where there are nested dependencies, such as time points within dyad (in repeated interaction experiments) or time points within chain (in iterated learning experiments). In addition, the tutorial gives recommendations for choices about model fitting. Annotated scripts in the online [Supplementary Data][1] provide the reader with R code to serve as a springboard for the reader’s own analyses. [1]: http://jole.oxfordjournals.org/lookup/suppl/doi:10.1093/jole/lzv003/-/DC1