Posted by
Robert L on
Mar 02, 2018; 2:05pm
URL: http://spssx-discussion.165.s1.nabble.com/testing-for-homoscedasticity-in-SPSS-tp5735562p5735613.html
This is off-topic, but I can't refrain from lifting a pet subject which was never really developed into something more concrete: don't you thing sound could be used to illustrate the "noice" in scatter plots? Imagine your favourite software connected to a tone generator where the output signals such as means would be represented by signals of different frequencies and variation by noice? Representing variation on a static 2-dimensional paper, is that really the best way? Sound could perhaps be a better tool for some cases. And then visually impaired might even have an advantage in interpreting the output?
Robert
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From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of Andy W
Sent: Friday, March 02, 2018 1:50 PM
To:
[hidden email]
Subject: Re: testing for homoscedasticity in SPSS?
Rich, the OP stated they were blind -- I'm not sure how exactly a blind person would be able to apply much of your advice.
I might go a bit of a different tact. For those who are not completely blind, but are visually impaired, you can export SPSS graphs as vector images, in which case you can zoom in and make the chart encompass your entire field of vision. The easiest way would be to export charts as PDF files from SPSS.
If this is the case, even if you need a screen reader but have some vision that might work out OK. Even in a blurry scatterplot you could assess heteroscedasticity. You don't need to be able to resolve each individual stroke of a point in the scatterplot to see the overall distribution of points. Some things like a histogram can never be really articulated in a set of statistics.
Either myself or other folks on this forum can help with constructing a chart template that makes this easier, such as larger fonts, bigger points, or higher contrast.
If you are entirely blind, I might suggest making a user request to SPSS -- a simple tool to export charts as STL files, which you can then 3d print.
(The smaller 3d printers anymore are not that expensive.) It would be a slow process, but tactically you could also easily identify heteroscedasticity.
Again I think that would also be very useful in general for histograms.
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Andy W
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Robert Lundqvist