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Re: st: testparm
At 04:02 AM 3/4/2008, Chiara Mussida wrote:
hi guys,
sorry for my previous formatted messages, but the other e-mail address
didn't give me the opportunity to write in palin text and I didn't
know how it processes my e-mail...sorry again (Thanks Marteen).
my question today is: if the null of testparm is rejected by the data
(my 4 categories related to geographical regions of residence) can I
quietly proceed with the estimation of (4 in my case) separated models
for the duration of unemployment? or are there other procedures/test
to perform?
all the best,
chiara
By way of analogy, suppose you had gender in your model, and its
effect was significant. Would you immediately leap to estimating
totally separate models for men and women? Probably not. The
significant effect of gender would tell you that the intercepts
differ for men and women, but it would not tell you whether the slope
coefficients for the independent variables differ by gender.
As Maarten notes, estimating separate models means you estimate a
bunch of parameters, perhaps unnecessarily so. This reduces your
likelihood of rejecting various null hypotheses even when you should reject.
Keep in mind, too, that in many cases people have all sorts of group
characteristics they could focus on, e.g. race, gender, region... If
you start estimating separate models for every group characteristic
of interest (and separate models for every combination of those
characteristics) you are going to get overwhelmed with parameters
pretty quickly. Generally, you want as much parsimony as possible in
your models.
In short, you have to break down and use theory to guide you at some
point. If you have good reasons for believing the models will be
very different across your groups, then estimating separate models
may be a good idea. But, you wouldn't base such a decision simply on
the finding that the intercepts differ across groups.
I'm a little more positive than Maarten about conducting tests to
help you make these decisions. A series of nested models, guided by
theory, can be very helpful in deciding how to proceed (as opposed
to, say, a mindless stepwise regression approach.) They can be
particularly helpful if you need to justify the decision NOT to
estimate separate models by groups, e.g. you can show that, say,
other than the intercepts, model coefficients do not significantly
differ by gender; or that you maybe you only need one or two
interaction terms (which your brilliant theory had previously
identified) as opposed to zillions of interactions. If there are
going to be dueling perspectives on how to proceed (e.g. a reviewer
says you should have estimated separate models for each group, or
conversely, that you shouldn't have) it is nice to have both
theoretical arguments and empirical evidence to defend what you did.
I outline some procedures for testing group differences in
http://www.nd.edu/~rwilliam/xsoc63993/l43.pdf
http://www.nd.edu/~rwilliam/xsoc63993/l51.pdf
http://www.nd.edu/~rwilliam/xsoc63993/l52.pdf
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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