The Quick Count and Election Observation

THE QUICK COUNT AND ELECTION OBSERVATION
Analyzing the Data by Strata
To this point, discussion has focused only on aggregate analysis; all of the available
data are considered together as a single block of data. There are, however,
compelling reasons to unpack the data when the vote count data (Form 2
data) are being analyzed. The standard practice is to divide the total sample
into components (strata) and to examine, in detail and separately, the data
from each of these different components. The strata, or segments of the total
sample, that are commonly identified for this purpose often take the following
form:
Strata 1 – all sample points within the capital city;
Strata 2 – data from sample points in all urban areas outside the
capital city; and
Strata 3 – the remaining points in the sample, from all rural areas
in the country.
Strata may be defined differently in different countries. Capital cities are nearly
always considered as a single strata for the simple reason that they are usually
the largest urban population concentration in the country and they may contain
as much as one third of the total population of the country (and so, one
third of the total sample). The precise definitions of the other relevant strata
require careful consideration. Selected strata should be relatively homogenous.
For example, they might be defined by a regionally distinct ethnic or
religious community in the country. They may have historically different political
loyalties. Alternatively, strata might include a part of the country with a
unique economy, such as a coastal region. For analytical purposes, however,
it is rarely useful to identify more than four strata within the total population.
Ideally, the strata should be of roughly equal size.
Standard practice is to
divide the total sample
into components (strata)
and to examine in
detail, and separately,
the data from each of
these different components.
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The strategy is to examine separately the evolution and sources of variation in
the data from the capital city (Strata 1), separately from the data coming from
urban areas outside of the capital city (Strata 2) and separately for data coming
from rural and remote areas (Strata 3).
There are a number of reasons for analyzing the data using this stratification
procedure. First, as has already been pointed out, data typically arrive at the
data collection centers at different rates from different regions. Second, it is
quite possible, and in fact quite likely, that different political parties will have
different strengths and levels of citizen support among different communities
in different parts of the country. Political parties often appeal to different class
interests (e.g., the professional/business middle class or agricultural workers)
and to different communal groups defined by language, religion, ethnicity or
age. The point is that these communities, or interests, are hardly ever distributed
evenly throughout the country. Those uneven distributions are usually
reflected in regional variations in support for parties and in the evolution of
quick count results. The following example illustrates this point: