  A new family of minimum distance estimators for binary logistic regression
models based on $\phi$-divergence measures is introduced. The so called "pseudo
minimum phi-divergence estimator"(PM$\phi$E) family is presented as an
extension of "minimum phi-divergence estimator" (M$\phi$E) for general sample
survey designs and contains, as a particular case, the pseudo maximum
likelihood estimator (PMLE) considered in Roberts et al. \cite{r}. Through a
simulation study it is shown that some PM$\phi$Es have a better behaviour, in
terms of efficiency, than the PMLE.
