<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class="">Dear Amber,<div class=""><br class=""><div class=""> Thanks for noting that old link in the manual. I have removed it and replaced it with a pointer to </div><div class=""><div style="margin: 0px 0px 0px 36px; text-indent: -36px;" class="">Malvern, D., Richards, B., Chipere, N., & Purán, P. (2004). <i class="">Lexical diversity and language development</i>. New York: Palgrave Macmillan.</div><div class=""><br class=""></div><div class="">In regards to sample size, VOCD requires 100 utterances. For smaller samples and more generally, you may wish to consider using MATTR, as described in my previous ChiBolts message on this topic. There I gave a reference to </div><div class=""><br class=""></div><div class="">Covington, M. A., & McFall, J. D. (2010). Cutting the Gordian knot: The moving-average type–token ratio (MATTR). Journal of Quantitative Linguistics, 17(2), 94-100. <br class=""></div><div class=""><br class=""></div><div class="">and you may wish to read the recent comparison of VOCD, TTR, and MATTR</div><div class=""><br class=""></div><div class=""><div style="margin: 0px 0px 0px 36px; text-indent: -36px;" class="">Fergadiotis, G., Wright, H., & Green, S. (2015). Psychometric evaluation of lexical diversity indices: Assessing length effects. <i class="">Journal of Speech, Language, and Hearing Research, 58</i>, 840. </div></div><div class=""><br class=""></div><div class="">My take-home from this is that people should only use TTR when comparing across samples of the same length and even then, VOCD or MATTR would be better. In general, researchers should prefer MATTR to VOCD. In CLAN, you run MATTR using this option in FREQ</div><div class=""><div class=""><br class="">+bN This option calculates the lexical diversity of a sample using the Moving Average Type-Token Ratio (MATTR). This index is based on a moving window that computes TTRs for each successive window of fixed length (N). Initially, a window length is selected (e.g., 10 words) and the TTR for words 1- 10 is estimated. Then, the TTR is estimated for words 2-11, then 3-12, and so on to the end of the text. For the final score, the estimated TTRs are averaged.<br class=""><br class=""><br class="">—Brian MacWhinney<br class=""></div></div><div class=""><br class=""></div></div></div></body></html>
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