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Re: st: RE: how to deal with categories?
This sound exactly like what I was looking for!
Many thanks for this info!
Andrea
On Jun 3, 2008, at 3:22 PM, philippe van kerm wrote:
The command -dummieslab- (available on the SSC archive) may be
useful to deal with your second question:
TITLE
'DUMMIESLAB': module to convert categorical variable to dummy
variables using label names
DESCRIPTION/AUTHOR(S)
dummieslab generates a set of dummy variables from a categorical
variable. One dummy variable is created for each level of the
original variable. Names for the dummy variables are derived from
the value labels of the categorical variable.
To install from within Stata:
ssc install dummieslab
Philippe
-----Original Message-----
From: [email protected] [mailto:[email protected]
] On Behalf Of Andrea Bennett
Sent: Tuesday, June 03, 2008 2:40 PM
To: [email protected]
Subject: st: how to deal with categories?
Dear all,
Right now I am wondering what is the better way to deal with
categorical information.
1.
What is about the best way to implement income groups into a
regression? E.g. as income has (usually) no upper limits, I tend to
generate an interaction term (dummy==1) if the individual is in the
highest income category (0 if else). Further, am I right in the
assumption that building categories is usually not sensible when the
number of observations is high? One issue I face is that very young
adults and very old adults are under-represented in the dataset
(meaning, not that many unique observations for these groups, sample
itself is good). Is there a rule of thumb what would be better,
building categories for all age-classes (increasing observations in
young/old group) or do not build classes at all (having more detailed
info)? It's clearly a trade-off but maybe there's some advice. I tend
not to use categories here, also because age-squared might be
important to have at hand, later.
2.
The "xi" command can help to make life less messy (in large data sets,
I think). But it seems to kill all my value labels in these
categorical groups! I could not find any option to tell "xi" to use
the already defined value labels. Is there a workaround at hand so
that the regression table will instead use the values defined (e.g.
for sex; 0==male, 1==female) as new variable names?
Many thanks for all your inputs,
Andrea
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