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.