1 Introduction
Welcome to the companion website to Chapter 23 “Analyzing aggregated linguistic data” by Benedikt Szmrecsanyi of Research Methods in Language Variation and Change!
What is aggregate data analysis? A good approach to finding an answer to this question is trying to describe, for instance, a particular regional language variety. For example, what differentiates New England English from Southern English? Is it the “Southern Drawl” or the fact that New England English tends to be non-rhotic, while Southern English is rhotic? These features are undoubtedly characteristic of the respective regional language varieties, but they offer only a partial description of them. Southern English is defined by much more than the “Southern Drawl”, which prohibits an analysis of the variety based on this feature alone. Therefore, in order to characterize any form of multidimensional object (i.e. regional or stylistic varieties etc.) one needs to “consider the bigger picture”, meaning he or she must try to account for as many features as possible, which may constitute the object in its complexity. And this is where aggregate data analysis comes into play. Instead of focusing on a single feature, aggregate data analysis considers a multidimensional object’s feature aggregates, which in the case of linguistics, for instance allows for a more comprehensive description of various dialects.
In his chapter, Szmrecsanyi points out that in the field of variationist linguistics, while there has been a far reaching tradition of “single-feature based studies”, feature aggregates, with the exception of three linguistic subfields, have been largely disregarded. This is not to say that feature-centered studies do not provide valuable linguistic insights; they most certainly do, however, as mentioned, these insights are inevitably limited by the initial research question’s focus.
Due to this lack of regard, Szmrecsanyi presents methodologies to conduct aggregate data analysis in variationist linguistics, outlining the range of applications as well as limitations and exemplifying these methodologies by means of three case studies.
This page is designed to complement Szmrecsanyi’s chapter. It offers, a short overview of the stages involved in conducting an aggregate analysis, a glossary of main terms used by Szmrecsanyi, a mini-project as well as a further reading section.
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