The Quick Count and Election Observation

THE QUICK COUNT AND ELECTION OBSERVATION
the range of values in one response category should not overlap with
those of other categories.
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• The efficiency test—Response categories should be designed to achieve
the maximimum efficiency by keeping the number of response categories
to a minimum. This has a significant impact on the volume of data that
are being transmitted. The fewer the number of response categories used
in a form, the faster and more accurately the data can be transmitted.
Furthermore, fewer key strokes are required to enter the data into the
computerized dataset.
What to Avoid
Lessons from past experience also suggest that some practices should be avoided.
These include:
• Open-ended questions—When designing observation forms it is very
tempting to want to include a few open-ended questions. For example,
if observers record the fact that the police might have intervened in election
day activities at a particular polling station, then it is natural to want
to know the details of what exactly happened. But the qualitative short
forms are not the best places to record this information; details of incidents
that could have a significant impact on the electoral process should
be gathered on separate forms. Answers to open-ended qualitative questions
might well produce “interesting findings,” but these kinds of data
are cumbersome. Uncategorized answers to open-ended questions are
a type of “anecdotal evidence,” and to be of any analytic help these kinds
of answers have to be re-coded into useful categories. The problem is
that it is very time consuming to recode such data. For all practical purposes
it is too difficult to both categorize and analyze these data within
very tight time constraints.
• False precision—Analysts want to work with precise results, but attempting
to achieve very high levels of precision is seldom warranted. Extra
precision usually involves collecting more data, which increases the load
on observers and communications systems. It also requires more time to
enter data that, in most cases, do not provide a substantive payoff when
it comes to the basic interpretation of the evidence. Consider the following
example related to the opening of polling stations:
We want to know at what time the first voter cast a ballot at a particular
polling station, so we ask the observer to record the exact
time, say 8:02 am. That may be the most precise result; however,
that level of precision is unnecessary. Moreover, this
specification of the question introduces time consuming complications
for both data entry and analysis. Suppose five polling
stations opened at the following times: 6:37; 9:58; 7:42; 11:59
and 12:10. To determine the average opening time involves