When a subgroup of a survey is analysed we can define a
__design
effect__ (DE) and a __
design factor
__ (DF) for the subgroup. The subgroup design effects
and factors will usually be different from that for the whole
survey.

There are two different ways in which design effects can
be defined for subgroups.

Remember that DEs and DFs compare a survey
design with what we would get from simple random sampling.
For subgroups we can think of two different ways of doing a
simple random sample:-

1. A simple random sample of the whole survey so that the
subgroups are represented in proportion to their size

2. A simple random sample for each subgroup selected by
taking a simple random sample of size equal to the number of
individuals in the survey within that subgroup.

The first method compares the current
design to a random sample over the whole population. Thus it
will give a low design effect (good precision) for subgroups
that are over represented in the survey.

The second method would give all subgroups
the design effect that they would get if a sub-survey were
designed consisting of just one subgroup at a time including
the same number of units that are in the current sample. So
in this case the DEs and DFs are affected only by the design
features within the subgroups.

Some packages give an option to select
which of these you want.