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Article published In: Journal of Pidgin and Creole Languages: Online-First Articles

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Measuring the similarity between languages

The case of creoles and non-creoles

ORCiD logo with link | Heinrich-Heine-Universität Düsseldorf, Germany
ORCiD logo with link | Heinrich-Heine-Universität Düsseldorf, Germany
Published online: 26 May 2025
DOI logo with link
https://doi.org/10.1075/jpcl.24026.pla
Abstract
In typology, statistical methods have been successfully used to assess similarities and differences between languages. In creole studies, the use of quantitative methods has been discussed controversially. In the debate many methodological aspects of the statistical models used have been criticized (e.g. Meakins, Felicity. 2022. Empiricism or imperialism: The science of creole exceptionalism. Journal of Pidgin and Creole Languages 37(1). 189–203. Google Scholar logo with link to Google Scholar; Bakker, Peter. 2023. Empiricism against imperialism: Science, dogma and the neocolonial heritage of creole studies. Reflections on Meakins (2022). Journal of Pidgin and Creole Languages. Google Scholar logo with link to Google Scholar). This paper presents an investigation of two methodological problems that have not been critically looked at so far: the question of which statistical models produce which results, and the question of how the amount of missing values in data sets influences the results. We present a study in which we tested different statistical models on 21 features from two arbitrarily chosen domains (‘Word Order’ and ‘Nominal Categories’) from the WALS (Dryer, Matthew S. & Martin Haspelmath. 2013. WALS Online (v2020.3). Zenodo Google Scholar logo with link to Google Scholar) and APiCS (Michaelis, Susanne Maria, Philippe Maurer, Martin Haspelmath & Magnus Huber (eds.). 2013. The atlas of pidgin and creole language structures. Oxford University Press, USA.Google Scholar logo with link to Google Scholar) data bases. It is demonstrated that different statistical methods yield similar results, and that different sample sizes do not dramatically influence the model outcomes.
Article outline
  • 1.Introduction
  • 2.Quantitative typology: Merits and pitfalls
    • 2.1Why compare (or not compare)?
    • 2.2Data
    • 2.3Statistical tools
    • 2.4The present study
  • 3.Methodology
    • 3.1Data
    • 3.2Coding
    • 3.3Sampling
    • 3.4Statistical modeling
  • 4.Results 1: Clustering
    • 4.1Phylogenetic networks
      • 4.1.1Neighbor-joining vs neighbor net networks
      • 4.1.2Neighbor-joining networks
      • 4.1.3Hierarchical cluster analysis
  • 5.Results 2: Predicting the type of language
    • 5.1Recursive partitioning and regression trees
    • 5.2Random forests
  • 6.Summary and discussion
  • Acknowledgements
  • Notes
  • References
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