The survey sample is constructed by YouGov Polimetrix, using a matched random sample technique. The firm begins with two lists, one of all consumers in the United States, covering approximately 95 percent of the adult population, and a list of people who have agreed to take surveys for YouGov Polimetrix as a part of their PollingPoint panel. All YouGov Polimetrix surveys are conducted on-line using this opt-in panel of respondents. For each list, YouGov Polimetrix has an extensive set of demographics.

In the first stage, a random sample of consumers is drawn. A list of key demographic variables is then recorded for every member of the sample. In essence, each individual drawn is represented as a cluster of demographic characteristics, including age, income, education, race, gender, longitude and latitude, etc. In the second stage, YouGov Polimetrix uses a matching algorithm to find the PollingPoint panelist who is the closest match to the person drawn off the consumer file. In this way a complete, matched random sample is constructed for all people in the sample.

The desired sample to be drawn for the CCES is a stratified national sample of registered and unregistered adults. We choose strata that guarantee that the study achieves adequate samples in all states. There are three sorts of strata in the sample: Registered and Unregistered Voters, State Size, and Competitive and Uncompetitive Congressional Districts.

By stratifying on registered and unregistered voters we can create a nationally representative sample of US adults using appropriate sample weights. Because the preferences of voters is of particular interest to many researchers, we oversample registered voters. Approximately three-fourths of US adults are registered to vote. In midterm elections approximately half of registered adults vote. The oversample of registered voters means that the actual voters in the sample approach one-half of the sample.

Stratification on state size is used to guarantee adequate sample sizes in small states. There are four strata for state size: one Congressional District states, two Congressional District states, three Congressional District states, and four or more Congressional District states.

Stratification on competitive congressional districts guarantees an adequate number of districts in which there are very active political campaigns in the fall election.

Altogether this sampling scheme minimizes the number of strata, so as to prevent mistakes, while guaranteeing adequate coverage of all relevant jurisdictions. Adding together each of the possible combinations results in 16 strata.

As a result, each state has sufficient coverage so that any team interested in the general politics of a given state can build a state-level survey of approximately 60 questions related to their state. In addition, if a sufficiently large group interested state politics emerges they may be able to trade questions across groups in such a way as to augment the Common Content. If a large enough number of teams agree to swap content in this way, then they can trade questions such that every time a respondent from a particular state is chosen in any survey within the Group then the question relevant to that particular state is used.

For example, suppose 15 teams have particular state level questions that they would like to ask. Say, Ohio wants to ask two questions about Ohio propositions, Michigan wants to ask two questions about a hot contest for Secretary of State in Michigan, Florida wants to ask two questions about the 2000 election, etc. Every time an Ohio respondent arises in any sample from among this Group’s members’ surveys, the Ohio questions are asked. Every time a Michigan respondent arises in any sample from among this Group’s surveys the Michigan questions are asked. And so forth.

In this way groups can exploit the sample design to develop unique state-level surveys. This strategy seems particularly attractive for larger states from which a disproportionate number of cases will likely be drawn.

CCES

Cooperative Congressional Election Study

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CCES 2010

CCES 2009

CCES 2008

CCES 2007

CCES 2006

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