Weighting Poll Results
As we discussed earlier, randomness is important to achieving a representative sample of the population. By using random dialing software, political polling organizations try to reach a perfectly randomized sample of respondents. But there are limits to the effectiveness of random dialing. For example, women and older Americans tend to answer the phone more often, which throws off the sex and age ratios of the sample. Instead of relying exclusively on random number dialing, political pollsters take the extra step of adjusting or weighting results to match the demographic profile of likely voters.
Notice that we said "likely voters," not the entire voting age population of the United States. That's an important distinction. If pollsters wanted to weight their results to match the entire voting age population, then they would adjust the results to match the latest census data. First they would distribute results geographically, keeping more responses from more populous states and cities. Then they would adjust results to match the demographic distributions of sex, age and race in America.
But if you want to achieve the most accurate political polling results, you need to filter the sample even further to weed out all of the respondents who are unlikely to vote. This is where the "art" of political polling comes into play. The best pollsters are the organizations who can develop poll questions and analysis models that separate the wheat from the chaff and isolate the responses of the most likely voters only [source: Zukin]. After all, in politics, your opinion only counts if you actually vote.
Each polling organization has its own system for identifying and weighting likely voters. ABC News uses a series of questions to gauge likeliness to vote:
- How old are you?
- Have you registered to vote?
- Do you intend to vote?
- Are you paying close attention to the race?
- Did you vote in past elections?
- Do you know the location of your polling station? [source: Langer]
Not all political polls are used to predict the outcome of elections. Sometimes pollsters want to gauge public opinion on different political issues, often in an attempt to compare the opinions of different demographic groups: old vs. young, Democrat vs. Republican, black vs. white. In that case, pollsters don't aim for a perfectly representative sample. Instead, they engage in oversampling to build a sample that includes an equal amount of respondents from each demographic. For example, if you want to poll the attitudes of white and black voters on a political issue, you would need to oversample black households because a randomized sample would only include 10 to 15 percent black respondents.
For lots more information on American politics and elections, see the related links on the next page.