VOCD manual

[Amber]

Dear Brian

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? 

Best wishes
Amber 

On Wednesday, 19 August 2015 17:22:53 UTC+1, Brian MacWhinney wrote:
>
> Dear Amber,
>
>     Thanks for noting that old link in the manual.  I have removed it and 
> replaced it with a pointer to 
> Malvern, D., Richards, B., Chipere, N., & Purán, P. (2004). *Lexical 
> diversity and language development*. New York: Palgrave Macmillan.
>
> 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 
>
> 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. 
>
> and you may wish to read the recent comparison of VOCD, TTR, and MATTR
>
> Fergadiotis, G., Wright, H., & Green, S. (2015). Psychometric evaluation 
> of lexical diversity indices: Assessing length effects. *Journal of 
> Speech, Language, and Hearing Research, 58*, 840. 
>
> 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
>
> +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.
>
>
> —Brian MacWhinney
>
>

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