At 07:54 PM 9/29/2008, Pirritano, Matthew wrote:
>The model is longitudinal. Baseline to 5 years of data. There are
>about 150 couples that we have data for at 5 years, starting with about 1000.
That's a repeated-measures study, unbalanced since you have varying
numbers of observations per subject. The proper generalization of
regression is a mixed-effects model, for which you need the MIXED
module in SPSS.
>Looking at the use of different coping strategies for men and women
>who are undergoing infertility treatments. The questions have
>already been combined into composites.
Is, then, the questionnaire intended to elicit the coping strategies
used, with composites for each of the coping strategies, as you
>Using the log transformed variables drastically reduces the
>chi-square for the model from over 3000 to around 1200. And I know
>that these types of developmental models often follow such a trend.
>That the log transformed relationship is more likely than a quadratic model.
At the point you, the subject specialist, know more than I, a
methodologist; especially, a methodologist who doesn't know the
study, nor anything about the subject. (I'd never work that blind, if
I had more responsibility than as a source on the list.)
You haven't even said what is your dependent variable or variables,
nor what are your independents.
>I'm talking to my collaborator tonight. I have prepared all of the
>logarithmic models. Maybe it makes sense to just forget about
>interpreting the betas and just go with the effects?
To emphasize again: Whatever transformation you carry out, implies a
certain form of model. Make sure you understand what that model is;
can describe it in your publication; and can argue that it is
>The log transformed relationship is more likely than a quadratic model.
Good; so you do have theoretical support.
Do think about >what< you are transforming. Log-transforming the
dependent variable, only, implies an exponential-growth model
(skipping over independent variables other than time). Such models
are often appropriate.
But, log-transforming independent >and< dependent variables implies a
product-of-powers model. Those are much less common. If you can argue
that it is appropriate in your case, go ahead. See a statistical
consultant, about precautions to estimate such models accurately.
There are pitfalls more subtle than dealing with 0 values.
>I suppose I could go back to the quadratic model, maybe I just got
>lured in by the ln model, and the quadratic wasn't that bad.
Again, if you have something like an exponential-growth model, fine.
If you have a product-of-powers model, fine, >if you have justification<.
>The really unpleasant thing about the quadratic model is how to
>create quadratic interaction terms! Don't you need to include the
>linear interaction (X times Covariate) and the quadratic version
>(Xsquared time Covariate) in order to look at the curvilinear effect
>of the quadratic interaction term.
It sounds like you're distinguishing two classes of independent
variables: whatever X is, and the covariates. I've been writing
without that distinction.
If X is a dependent variable of the first kind, and C is a covariate,
the quadratic terms are X**2; C**2; and X*C. Not X**2*C, which is a
third-order term. (Add the exponents of all the factors.)
But you don't have to estimate a >saturated< quadratic model, with
all second-order terms. You can include quadratic terms for only
those variables where you expect a curvilinear effect; and
cross-terms ('interaction terms') for only those pairs of variables
where you expect an interaction.
And here, your knowledge of your study must take over.
-Onward, in peace,
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