> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
> statalist@hsphsun2.harvard.edu] On Behalf Of Scott Cunningham
> Question: The coefficient on sr is -.6870002, and it is the variable
> of interest. Am I correct in the following interpretations:
>
> Q1. The constant (_cons) represents the log of the mean number of
> sex partners for the reference cell, which in this case is Black men
> living in homes where parents are not married (hhd1=1 if biological
> parents are married). Since exp{.5387156)=1.7138, we see that on the
> average these men have 1.7 sex partners at this point in their
> lives. Is this the correct interpretation?
Holding all other variables constant.
> Q2. The hhd1 variable reflects the state of the family in which the
> Black male lives. As we move from hhd1=0 to hhd1=1, the log of the
> mean decreases by .7, which means that the number of sex partners
> gets multiplied by exp{-0.300639)=0.740345. This means that Black
> males with married biological parents have 25% fewer sex partners
> than their counterparts whose parents are not married. Is this the
> correct interpretation?
Yes, holding all other variables constant.
> Q3. The sr variable is continuous. It is the ratio of eligible
> Black males (of a certain age range) to eligible Black females (of
> like age range) at the state level, and will take on a value from as
> low as 0.3 to 2.5. I am unsure of how to interpret the marginal
> effect of a change in the sex ratio ("sr") on sex partners. Am I
> correct that a one unit increase in the sex ratio causes recent sex
> partners for Black males to fall by 50%? If so, what is a "one unit"
> when we are talking about a continuous random variable? Is it a one
> unit increase in the standard deviation? I've been unable to find
> this information from my reference books, and do not currently own
> the Stata book on categorical variables. But if anyone can provide
> basic help here, I'd appreciate it.
One unit change would be increasing the sex ratio by +1. Perhaps the
marginal effects would be of more interest than the factor change. Or
calculate the change in number of sex partners for a discrete change in sex
ratio that is of interest.
While you are waiting for "Regression Models for Categorical Dependent
Variables Using Stata" to arrive, you might check to see if a library near
you has J. Scott Long's "Regression Models for Categorical and Limited
Dependent Variables"
Scott
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