How to analyze linguistic change using mixed models, Growth Curve Analysis and Generalized Additive Modeling

IF 2.1 0 LANGUAGE & LINGUISTICS
Bodo Winter, Martijn B. Wieling
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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
如何使用混合模型、增长曲线分析和广义加性建模来分析语言变化
在进行语言进化领域的实证研究时,随时间的变化是一个固有的维度。本教程向读者介绍混合模型,增长曲线分析(GCA)和广义加性模型(GAMs)。这些方法非常适合分析随时间的非线性变化,其中存在嵌套的依赖关系,例如dyad中的时间点(在重复的交互实验中)或chain中的时间点(在迭代的学习实验中)。此外,本教程还提供了关于模型拟合选择的建议。在线[补充数据][1]中的注释脚本为读者提供了R代码,作为读者自己分析的跳板。[1]: http://jole.oxfordjournals.org/lookup/suppl/doi: 10.1093 /乔/ lzv003 /——/ DC1式
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Language Evolution
Journal of Language Evolution Social Sciences-Linguistics and Language
CiteScore
4.50
自引率
7.70%
发文量
8
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