Measuring Lexical Diversity in Narrative Discourse of People with Aphasia

Autor/inn/en	Fergadiotis, Gerasimos; Wright, Heather H.; West, Thomas M.
Titel	Measuring Lexical Diversity in Narrative Discourse of People with Aphasia
Quelle	In: American Journal of Speech-Language Pathology, 22 (2013) 2, (12 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1058-0360
DOI	10.1044/1058-0360(2013/12-0083)
Schlagwörter	Aphasia; Language Processing; Semantics; Content Analysis; Measures (Individuals); Factor Analysis; Statistical Analysis; Test Validity; Construct Validity + Suchen Sie Ihr Suchwort? Expressive Aphasie; Aphasie; Sprachverarbeitung; Semantik; Inhaltsanalyse; Messdaten; Faktorenanalyse; Statistische Analyse; Testvalidität
Abstract	Purpose: A microlinguistic content analysis for assessing lexical semantics in people with aphasia (PWA) is lexical diversity (LD). Sophisticated techniques have been developed to measure LD. However, validity evidence for these methodologies when applied to the discourse of PWA is lacking. The purpose of this study was to evaluate four measures of LD to determine how effective they were at measuring LD in PWA. Method: Four measures of LD were applied to short discourse samples produced by 101 PWA: (a) the Measure of Textual Lexical Diversity (MTLD; McCarthy, 2005), (b) the Moving-Average Type-Token Ratio (MATTR; Covington, 2007), (c) D (McKee, Malvern, & Richards, 2000), and (d) the Hypergeometric Distribution (HD-D; McCarthy & Jarvis, 2007). LD was estimated using each method, and the scores were subjected to a series of analyses (e.g., curve-fitting, analysis of variance, confirmatory factor analysis). Results: Results from the confirmatory factor analysis suggested that MTLD and MATTR reflect LD and little of anything else. Further, two indices (HD-D and D) were found to be equivalent, suggesting that either one can be used when samples are greater than 50 tokens. Conclusion: MTLD and MATTR yielded the strongest evidence for producing unbiased LD scores, suggesting that they may be the best measures for capturing LD in PWA. (Contains 3 tables, 4 figures and 1 footnote.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10