On Sun, 15 Feb 2009, achristoffersen wrote:
Dear Sirs and madams :-)
I am trying to teach myself multidimensional scaling. To that effect I have
collected a survey asking people to rank 10 philosophers and politicians
according to their preference. I have collected 61 answers. The data is
organized in ten columns and 61 rows. the columns are "choice_1",
"choice_2", "choice_3" etc. The cells is the name of the philosopher
I guess I need to put the data in some other format, e.g. with colloumns:
"philospher_1", "philospher_2", "philospher_3" etc. and then have the cell
hold the particular ranking (score) for that philospher (i.e. a number
between 1:10)
I guess such a transformation would also allow me to do clusteranalysis? -
But how to do it???
Something like
dat <- t( apply( your.data, 1 , order ) )
colnames(dat) <- paste( 'philosopher', 1:10, sep='_' )
HTH,
Chuck
Anyways: what I have done so far is to compute a 10*10 matrix in a
spreadsheet application. I do this by
countif(range_choice1=philospher1)*10 for each philospher.
In “range_choice2” I multiply by 9, and in “range_choice3” I multiply with 8
etc.
The corresponding matrix I import to r and do
dist(t(matrix)
and then I use cmdscale$points to draw a plot. It looks nice but I am almost
sure I'm doing it wrong. And I would certainly like not having rely on a
spreadsheet.
So my question is: how to transform the data, and is it true that my current
'spreadsheet' method is wrong? Also: should consider discarting some data,
e.g. only using the top 3 choices?
Cheers in advance.
Andreas
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Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
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