The Quick Count and Election Observation

CHAPTER SEVEN: COLLECTING AND ANALYZING QUICK COUNT DATA
116 In one country, different parties have different levels of support within
different demographic segments of a population. Consequently,
shifts in the balance of support for political parties during the evolution
of quick count results (T1 ….Tn) simply reflect what is technically
called different “composition effects.” Party A may appeal to the young,
and Party B to older citizens. If there are more young people living in
the capital city, then “early” results from the quick count might show
that Party A is ahead. These aggregate results change as data arrive
from those parts of the country where there are higher concentrations
of older people. In preparing for the analysis of quick count data, analysts
should become familiar with what these variations might be.
Census data, data from previous elections and knowledge of the historical
bases of support for the parties are all useful sources for providing
By analyzing the
different strata separately,
analysts can
ascertain more reliably
the point of stability.
Once the data have
stabilized within all
strata, the addition of
new data cannot
change the distribution
of the vote for the
country as a whole.
analysts with this kind of background information.
By analyzing the different strata separately, analysts can ascertain more reliably
the point of stability. In fact, the most reliable, and conservative, practice
is to analyze the data to determine the point of stability for each of the strata.
Statistically, by following exactly the same procedures that are outlined in
Chapter Five, it is useful to calculate what are the margins of error for each of
the strata. With that calculation in hand, analysts can determine what are the
minimum number of data points required within each strata to satisfy a margin
of error of, say, 1 percent for each of the strata. Using that guideline,
analysts can determine quite precisely just how many sample points are required
from each strata for the data within that strata to stabilize. When the point of
stability is reached for each of the strata, then the addition of new sample data
will have no impact on the distribution of the vote within each strata. Once
the data have stabilized within all strata, the addition of new data cannot
change the distribution of the vote for the country as a whole. The aggregate
result, after all, is the sum of the stratified results. Figure 7-4 provides a graphic
summary of how vote counts aggregately “stabilize” during an analysis of
data from “takes” T1…Tn.
Notice in Figure 7-4, that the early results (T1, T2 and T3) show considerable
variation in the distribution of support for Party A and Party B. That variation
can be explained by a combination of factors. First, the data that arrive first
come from the capital city, and support for Party A is higher in the capital city.
Second, the effective sample, at T1, is very small, and it produces estimates
that are both biased (capital city results) and have high margins of error. By
T4, as the effective sample size increases, the differences in the balance of vote
support for the parties is declining. At T4, Party A and Party B are in a close
battle, and Party B appears to be catching Party A. By T5, Party B’s popular
strength in the rural areas is beginning to show. The effect is to place Party B
ahead of Party A, and by T6 the data appear to have stabilized.