Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Sign In to gain access to subscriptions and/or personal tools.
Language Testing
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Jarvis, S.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Short texts, best-fitting curves and new measures of lexical diversity

Scott Jarvis

Ohio University, jarvis{at}ohio.edu

Following up on recent work by Malvern and Richards (1997, this issue; McKee et al., 2000) concerning the measurement of lexical diversity through curve fitting, the present study compares the accuracy of five formulae in terms of their ability to model the type-token curves of written texts produced by learners and native speakers. The most accurate models are then used to consider unresolved issues that have been at the forefront of past research on lexical diversity: the relationship between lexical diversity and age, second language (L2) instruction, L2 proficiency, first language (L1) background, writing quality and vocabulary knowledge. The participants in the study comprise 140 Finnish-speaking and 70 Swedish-speakinglearners of English, and an additional group of 66 native English speakers. The data include written narrative descriptions of a silent film, and the results show that two of the curve-fitting formulae provide accurate models of the type-token curves of over 90% of the texts. The texts for which accurate models were obtained were subjected to further analyses, and the results indicate a clear relationship between lexical diversity and amount of instruction, but a more complicated relationship between lexical diversity and L1 background, writing quality and vocabulary knowledge.

Language Testing, Vol. 19, No. 1, 57-84 (2002)
DOI: 10.1191/0265532202lt220oa
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 Applied LinguisticsHome page
G. Yu
Lexical Diversity in Writing and Speaking Task Performances
Applied Linguistics, June 4, 2009; (2009) amp024v1.
[Abstract] [Full Text] [PDF]

Home page
 Assessment for Effective InterventionHome page
C. Coleman, N. Gregg, L. McLain, and L. W. Bellair
A Comparison of Spelling Performance Across Young Adults With and Without Dyslexia
Assessment for Effective Intervention, March 1, 2009; 34(2): 94 - 105.
[Abstract] [PDF]

Home page
 Language TestingHome page
P. M. McCarthy and S. Jarvis
vocd: A theoretical and empirical evaluation
Language Testing, October 1, 2007; 24(4): 459 - 488.
[Abstract] [PDF]

Home page
 J Learn DisabilHome page
N. Gregg, C. Coleman, M. Davis, and J. C. Chalk
Timed Essay Writing: Implications for High-Stakes Tests
J Learn Disabil, August 1, 2007; 40(4): 306 - 318.
[Abstract] [PDF]