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