Data and code to run the DCA and GLMM analyses in the study described in the article "Language change in multidimensional space: New methods for modelling linguistic coherence", by Xia Hua, Felicity Meakins, Cassandra Algy and Lindell Bromham, published in Language Dynamics and Change (2021).

These are the supplementary materials for an article published in Language Dynamics and Change, entitled 'Language change in multidimensional space: New methods for modelling linguistic coherence', by Xia Hua, Felicity Meakins, Cassandra Algy and Lindell Bromham, with DOI: 10.1163/22105832-bja10015. Linguistic coherence – the co-variation of language variants within speaker repertoires – has been proposed as a key process driving the divergence of language dialects. Previous studies on coherence have been often limited by dataset sizes and analyses. We analyze the use of 185 variables across 78 speakers from the Gurindji community in Australia. We use two multivariate statistical approaches to test whether clusters of variables co-vary with generation, family, household, exposure to Gurindji language speakers and education. Using Discriminant Correspondence Analysis, we find generation is the strongest grouping factor of speakers and co-varies with clusters of variants. Using the Generalized Linear Mixed Model, we find these clusters of variants not only represent a gradual loss of Gurindji language use across generations, but also contribute to distinct patterns of language usage in the different generations. Our study demonstrates the use of multivariate analyses on big datasets to identify sociolects, an important step in linking the ‘micro-level’ processes to the ‘macro-level’ outcomes.These datasets contain the input data and codes to run the DCA and GLMM analyses in this study.

本数据集为发表于《语言动态与变化》杂志上的文章《多维空间中的语言变化：构建语言连贯性新方法》的补充材料，该文章由夏华、费莉西蒂·米金斯、卡珊德拉·阿尔吉和林德尔·布罗姆汉共同撰写，DOI号为10.1163/22105832-bja10015。语言连贯性（即语言变体在说话者语料库中的共变）被提出为驱动语言方言分化的重要过程。以往对连贯性的研究往往受限于数据集规模和分析方法。本研究分析了来自澳大利亚古尔吉尼社区的78位说话者所使用的185个变量。我们采用两种多元统计分析方法来检验变量簇是否与世代、家族、家庭、接触古尔吉尼语说话者和教育程度共变。运用判别对应分析，我们发现世代是说话者最强分组因素，并与变体簇共变。运用广义线性混合模型，我们发现这些变体簇不仅代表着古尔吉尼语使用在世代间的逐渐减少，还促进了不同世代中语言使用模式的独特性。本研究展示了在大型数据集上运用多元分析方法以识别社会方言，这是将‘微观层面’过程与‘宏观层面’结果相联系的重要步骤。这些数据集包含了运行本研究中的DCA和GLMM分析的输入数据和代码。