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Word Associations

Tess Fitzpatrick Orcid Logo

The Encyclopedia of Applied Linguistics, Volume: Lexis

Swansea University Author: Tess Fitzpatrick Orcid Logo

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DOI (Published version): 10.1002/9781405198431

Abstract

Word association data comprises responses supplied by participants to a series of cue words. Such data has the potential to reveal information about how the mental lexicon is organized, and how lexical items are retrieved. To maximize this potential, the data must be collected, processed, and analyz...

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Published in: The Encyclopedia of Applied Linguistics
ISBN: 9781405198431
Published: Wiley 2025
Online Access: https://onlinelibrary.wiley.com/doi/10.1002/9781405198431.wbeal1283.pub2
URI: https://cronfa.swan.ac.uk/Record/cronfa71246
Abstract: Word association data comprises responses supplied by participants to a series of cue words. Such data has the potential to reveal information about how the mental lexicon is organized, and how lexical items are retrieved. To maximize this potential, the data must be collected, processed, and analyzed in a principled way. This necessitates careful attention to cue word selection, participant selection, and method of administration, as well as to the type of analysis employed. Typically, analysis will follow one of the two methods: the stereotypy method assesses each individual response according to how “typical” it is of group responses to the same cue. Norms lists, ranking responses according to their frequency of occurrence, are compiled to support this method. The second common form of analysis entails assigning responses to categories according to their relationship to the cue word. Categories vary, but might include synonyms, collocations, orthographic similarity, and so forth. In applied linguistics, word association data has mostly been used to investigate learners' developing lexicons. There is evidence to suggest that orthographic/phonological connections are the first to develop, followed by meaning-based and collocational links. Word association research is compatible with graph and network theory, and these offer useful perspectives on the dynamic mental lexicon.