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The Social Origins of Language

By Daniel Dor

Presents a new theoretical framework for the origins of human language and sets key issues in language evolution in their wider context within biological and cultural evolution


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Preposition Placement in English: A Usage-Based Approach

By Thomas Hoffmann

This is the first study that empirically investigates preposition placement across all clause types. The study compares first-language (British English) and second-language (Kenyan English) data and will therefore appeal to readers interested in world Englishes. Over 100 authentic corpus examples are discussed in the text, which will appeal to those who want to see 'real data'


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Free Access 4 You

Free access to several Brill linguistics journals, such as Journal of Jewish Languages, Language Dynamics and Change, and Brill’s Annual of Afroasiatic Languages and Linguistics.


Academic Paper


Title: Our statistical intuitions may be misleading us: Why we need robust statistics
Author: Jenifer Larson-Hall
Email: click here to access email
Institution: Kyushu University
Linguistic Field: Discipline of Linguistics; Language Acquisition
Abstract: Most academics' intuitions about statistics follow those of naive laypeople – that is, we often think that a sample should reflect the population characteristics more closely than it does, and expect less variability in samples than is truly found in them. These intuitions may prevent us from understanding why modern developments in statistics are needed. Another intuition most researchers hold is that it is better to be conservative when performing statistics, and this may involve adjusting p-values for multiple tests, using more conservative post hoc tests, or setting an alpha value lower than .05 when possible. However, the more we try to control against making an error in being overeager to find differences, the stronger the probability that we will make an error in not finding differences that actually exist. These two forces need to be counterbalanced, and this involves increasing the power of our tests. Robust statistics can increase the power of statistical tests to find real differences. I discuss the need for robust techniques to avoid reliance on classical assumptions about the data. Examples of robust analyses with t-tests, correlation, and one-way ANOVA are shown.

CUP at LINGUIST

This article appears in Language Teaching Vol. 45, Issue 4, which you can read on Cambridge's site or on LINGUIST .



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