Assuming you have coded everything correctly, I would look at something
called complete or quasi-complete separation. These conditions often
lead to grossly inflated coefficients.
Claudiu D. Tufis wrote:
Hi,
I have a multiple logistic regression. Among the predictors, I have 6
Something from Greenland and Rothman's book Modern Epidemiology (page 258)
may apply here:
there is a hallmark sysmptom of the bias that arises when stratification
has exceeded the limits of the data: The exposure effect estimates begin to
get further and further from the null as more variables
Hi,
I have a multiple logistic regression. Among the predictors, I have 6
variables that represent the dummies for an interaction term (the seventh is
the reference category and is not included in analysis). I have obtained for
five of these variables extremely large coefficients:
On 25 Jan 2002 08:13:41 -0800, [EMAIL PROTECTED] (Claudiu D. Tufis)
wrote:
Hi,
I have a multiple logistic regression. Among the predictors, I have 6
variables that represent the dummies for an interaction term (the
seventh is the reference category and is not included in analysis). I
