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 understand them?

>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 theoretically reasonable.

>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, Richard

===================== To manage your subscription to SPSSX-L, send a message to LISTSERV@LISTSERV.UGA.EDU (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD