<div dir="ltr">Thanks Leonid! Going to download a newer version. <br><br>Best<br>Amber <br><br>On Wednesday, 19 August 2015 18:56:02 UTC+1, Spektor, Leonid: CMU wrote:<blockquote class="gmail_quote" style="margin: 0;margin-left: 0.8ex;border-left: 1px #ccc solid;padding-left: 1ex;"><div style="word-wrap:break-word">Amber,<div><br></div><div><span style="white-space:pre"> </span>CHECK does not look at %mor tier. KIDEVAL will check word's one to one correspondence between %mor tier and main speaker tier, But, if you have already ran KIDEVAL with your current version of CLAN without any errors, then the latest version of CLAN will not find errors either. You do not need to re-MOR your data to use MATTR analyses, because FREQ is only looking at main speaker tiers and is ignoring %mor tiers. But, if you want to get more accurate results from KIDEVAL, for example, then you should get new MOR grammar and re-MOR your data. In fact you should do that even if you do not update your CLAN, because we constantly improve MOR grammar and this will give you more accurate results.</div><div><div>
<br><span style="color:rgb(0,0,0);font-family:'Lucida Grande';font-size:12px;font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;display:inline!important;float:none">Leonid.</span>
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<br><div><blockquote type="cite"><div>On Aug 19, 2015, at 13:14, Amber <<a href="javascript:" target="_blank" gdf-obfuscated-mailto="nVTaX5Z2CwAJ" rel="nofollow" onmousedown="this.href='javascript:';return true;" onclick="this.href='javascript:';return true;">ambal...@gmail.com</a>> wrote:</div><br><div><div dir="ltr">Dear Brian<br><br>Thank you. When I have gone to run the +bN command, it is not working. I am thinking that I need to download a newer version of CLAN (I can see from previous posts that MATTR has only recently been implemented). However, I want to avoid having to re-MOR and check all my transcripts to ensure their compatibility with the newer version of the software. Is there anyway to update my current CLAN software (without downloading the newer version) so that will allow me to run +bN? <br><br>Best wishes<br>Amber <br><br>On Wednesday, 19 August 2015 17:22:53 UTC+1, Brian MacWhinney wrote:<blockquote class="gmail_quote" style="margin:0;margin-left:0.8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word">Dear Amber,<div><br><div> Thanks for noting that old link in the manual. I have removed it and replaced it with a pointer to </div><div><div style="margin:0px 0px 0px 36px">Malvern, D., Richards, B., Chipere, N., & Purán, P. (2004). <i>Lexical diversity and language development</i>. New York: Palgrave Macmillan.</div><div><br></div><div>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><br></div><div>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></div><div><br></div><div>and you may wish to read the recent comparison of VOCD, TTR, and MATTR</div><div><br></div><div><div style="margin:0px 0px 0px 36px">Fergadiotis, G., Wright, H., & Green, S. (2015). Psychometric evaluation of lexical diversity indices: Assessing length effects. <i>Journal of Speech, Language, and Hearing Research, 58</i>, 840. </div></div><div><br></div><div>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><div><br>+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><br><br>—Brian MacWhinney<br></div></div><div><br></div></div></div></div></blockquote></div><div><br></div>
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