[BlindRUG] Measures of modality: understanding distribution shapes

[Jude DaShiell]

Richard Pring wrote a book on trend analysis and gave the criteria for 
several different chart shapes in that book too.  The criteria was given 
in both text and shown as graphics if I remember correctly. 
Unfortunately I don't remember the title of that book or other details 
now.  It most certainly is not in any accessible form by now but may 
provide a useful reference.  The technicians that do trend analysis 
developed a language to describe their graphics and for large data sets 
it may be helpful to provide a key with each of the different kinds of 
chart types found in the data with textual definitions of those terma 
along with the data set and a narrative of the data set.  The narrative 
would describe in terminology taken from the provided key which chart 
types apply in what order for for what percentiles of the data in the 
data set starting from beginning and moving to the end of the data set. 
No sonification or tactile display then need be used.  Discussion of the 
implications of each chart type described in the narrative can then be 
left to the audience and presenters involved.

On Thu, 30 Jul 2015, Godfrey, Jonathan via BlindRUG wrote:

> Date: Thu, 30 Jul 2015 03:57:03
> From: "Godfrey, Jonathan via BlindRUG" <blindrug at nfbnet.org>
> To: Blind R Users Group <blindrug at nfbnet.org>
> Cc: "Godfrey, Jonathan" <A.J.Godfrey at massey.ac.nz>
> Subject: Re: [BlindRUG] Measures of modality: understanding distribution
>     shapes
> 
> Hi,
>
> This is a great question.
>
> Personally, I would be hoping the sample size is sufficient to allow me to increase the number of bins in a histogram to convince myself that the presence of bimodality is real. I can get the information from the VI() command on the histogram creation command. For example:
> VI(hist(x))
>
> I'm still trying to find a way of conveying a density curve to a blind audience. It's very easy to follow a tactile representation of a density, but conversion to text is proving challenging. I suspect this is one situation where sonification of the curve might also prove to be an answer.
>
> Cheers,
> Jonathan
>
>
>
> -----Original Message-----
> From: BlindRUG [mailto:blindrug-bounces at nfbnet.org] On Behalf Of Gjalt-Jorn Peters via BlindRUG
> Sent: Thursday, 30 July 2015 6:29 p.m.
> To: BlindRUG at nfbnet.org
> Cc: Gjalt-Jorn Peters
> Subject: [BlindRUG] Measures of modality: understanding distribution shapes
>
> Dear BlindRUG readers,
>
> I'm working on revising our first statistics course (in a psychology curriculum). We're updating it such that R can be used, to make it accessible to blind students.
>
> I'm currently trying to figure out how to let blind students assess distribution 'shapes'. They can compute skewness and kurtosis, but it's also necessary to assess modality (how many 'mountains' there are). Is any of you familiar with any measures for this?
>
> How do you normally assess the 'shape' of a distribution?
>
> Thank you very much in advance, kind regards,
>
> Gjalt-Jorn Peters
> Dutch Open University
>
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>

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