
Stratified
Sampling
In
a stratified sample the sampling frame is divided into nonoverlapping
groups or strata, e.g. geographical areas, agegroups, genders. A sample is
taken from each stratum, and when this sample is a simple random sample
it is referred to as stratified random sampling.

Advantages


Stratification will always achieve greater precision
provided that the strata have been chosen so that members of the
same stratum are as similar as possible in respect of the
characteristic of interest. The bigger the differences
between the strata, the greater the gain in precision. For
example, if you were interested in Internet usage you might
stratify by age, whereas if you were interested in smoking you
might stratify by gender or social class.

It
is often administratively convenient to stratify a
sample. Interviewers can be specifically trained to
deal with a particular agegroup or ethnic group, or
employees in a particular industry. The results from
each stratum may be of intrinsic interest and can be
analysed separately.
It
ensures better coverage of the population than simple
random sampling.

Disadvantages

Choice
of Sample Size for each Stratum
In general the
size of the sample in each stratum is taken in proportion to the size of the
stratum. This is called proportional allocation. Suppose
that in a
company there are the following staff:
male, full time 
90 
male, part time 
18 
female, full time 
9 
female, part time 
63 
and
we are asked to take a sample of 40 staff, stratified according to the
above categories.
The first step is to find the total number of staff
(180) and calculate
the percentage in each group.
% male, full time = ( 90 / 180 ) x 100 =
0.5 x 100 = 50
% male, part time = ( 18 / 180 ) x100 =
0.1 x 100 = 10
% female, full time = (9 / 180 ) x 100 =
0.05 x 100 = 5
% female, part time = (63/180)x100 =
0.35 x 100 = 35
This
tells us that of our sample of 40,
