drlucyasher wrote:
The questionnaire has a section which contains a particular issue and then
questions which are related to this issue (and potentially to each other):
1) importance of the issue (7 ordinal categories from -3 to +3)
2) impact of the impact (7 ordinal categroies from -3 to +3)
3) percentage affected by the issue (11 ordinal categories from 0, 0-10,
20-30, 30-40.90-100)
I also have three participant predictive factors:
Gender (M/F)
Age (continuous scale)
Sector (6 nominal categories)
Gender and Sector are clear; convert these to factors, preferably giving
them meaningful names (m/f, east, west), and everything will be treated
correctly by most r function. Age is also clear, leave as is.
There will be considerably discussion how to code the scores. If these are
not heavily skewed (all -3), in some fields it is accepted to treat these as
continuous. Frank Harrell would argue against it.
I have revised too many manuscripts in both directions, so my opinion
depends on the paper where you publish it.
Anyway, Frank Harrel's lrm in Design might give you a starter. There is also
a well-known book by him on the subject.
Dieter
--
View this message in context:
http://www.nabble.com/Ordinal-response-model-tp25856728p25860439.html
Sent from the R help mailing list archive at Nabble.com.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
