The Quick Count and Election Observation

CHAPTER FIVE: STATISTICAL PRINCIPLES AND QUICK COUNTS
Quick count methodology
allows a group to
demonstrate why election-day
processes can
be considered fair, or
the extent to which they
have been unfair.
58 of opinion or open to partisan interpretation; they are demonstrable and universally
accepted. It is precisely because these principles are scientifically based
that quick count organizers can make authoritative claims about election outcomes.
It is one thing to claim that an election has been fair or unfair. Quick
count methodology allows a group to demonstrate why election-day processes
can be considered fair, or the extent to which they have been unfair.
Reliability and Validity
Statements made about election-day processes are only as strong as the data
upon which they are based. Consequently, it is important to take quite deliberate
steps to ensure that the data collected meet certain standards. One is
that the quick count data themselves have to be “robust.” That is, the data
have to be both reliable and valid.
Data are considered reliable when independent observers watching the same
event (the vote count) and using the same measuring instrument (the observer
form) evaluate that event in exactly the same way. A simple example
illustrates the point:
Three different people (A, B and C) repeatedly measure the height of
a fourth person (Z) on the same day. The measure of that person’s
height would be considered reliable if all three observers (A, B and C)
using the same measuring instrument (a standard tape measure) produced
exactly the same results in their measure of Z’s height.
It is important to take
quite deliberate steps
to ensure that the
data collected meet
certain standards. One
is that the quick count
data themselves have
to be “robust.”
The very same principle applies to quick count data collection; it is essential that
both indicators and measurements are reliable. The information produced by
observers should not change because of poor indicators, inadequate measurement
instruments (an elastic measuring tape) or poor procedures—nor should
the results vary depending upon who is doing the measuring. Reliable results
will vary only when there are genuine changes in the phenomenon that is being
measured. Reliable data, then, are data that can be independently verified.
Quick count data should also be valid. Validity concerns how well any indicator
used actually fits the intended concept that is being measured. A measure
is considered valid if the indicator used for measurement corresponds exactly,
and entirely, to the scope and content of the object that is being measured.
The previous example can be extended to illustrate the point:
Three additional observers (D, E and F) are asked to report the size of
the same person, Z. D and E might report that Z, who is six feet tall,
is big, whereas F might say that Z is medium. The problem is that the
concept of size is ambiguous and open to different interpretations; for
some people it might mean more than just height; therefore, size lacks
validity. D might consider Z big because Z is much taller than D. E