Abstract
In this paper we use data from the World Atlas of Language Structures (WALS) for a balanced sample of 100 languages and 60 different features. The values for all those features are interpreted as binary complexity variables, which are subject to statistical correlation analyses (looking for the possible existence of complexity trade-offs). To do that we use standard correlation coefficients but also partial correlation coefficients, which control for the effect of other linguistic and non-linguistic factors (geographic location, genetic affiliation, population size). We end up with the conclusion that several important complexity trade-offs exist, but they tend to be hidden by other elements. Their most evident signals are the facts that negative correlations between complexity variables increase when we control for other factors, and that any language is more complex than any other language in the sample in at least one feature.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
