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About the Surveys
Results for the August 2004 New Interest Index survey are based on telephone interviews conducted under the direction of Princeton Survey Research Associates International among a nationwide sample of 1,512 adults, 18 years of age or older, during the period August 5-10, 2004. For results based on the total sample, one can say with 95% confidence that the error attributable to sampling is plus or minus 3 percentage points. For results based on registered voters (N=1,166) the sampling error is plus or minus 3.5 percentage points. For results based on either Form 1 (N=739) or Form 2 (N=773), the sampling error is plus or minus 4 percentage points. If based on Form 1 or Form 2 registered voters only (N=569 or N=597), the sampling error is plus or minus 4.5 percentage points.
In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls.
Survey Methodology in Detail
The sample for this survey is a random digit sample of telephone numbers selected from telephone exchanges in the continental United States. The random digit aspect of the sample is used to avoid “listing” bias and provides representation of both listed and unlisted numbers (including not-yet-listed). The design of the sample ensures this representation by random generation of the last two digits of telephone numbers selected on the basis of their area code, telephone exchange, and bank number.
The telephone exchanges we
re selected with probabilities proportional to their size. The first eight digits of the sampled telephone numbers (area code, telephone exchange, bank number) were selected to be proportionally stratified by county and by telephone exchange within county. That is, the number of telephone numbers randomly sampled from within a given county is proportional to that county’s share of telephone numbers in the U.S. Only working banks of telephone numbers are selected. A working bank is defined as 100 contiguous telephone numbers containing three or more residential listings.
The sample was released for interviewing in replicates. Using replicates to control the release of sample to the field ensures that the complete call procedures are followed for the entire sample. The use of replicates also insures that the regional distribution of numbers called is appropriate. Again, this works to increase the representativeness of the sample.
At least 10 attempts were made to complete an interview at every sampled telephone number. The calls were staggered over times of day and days of the week to maximize the chances of making a contact with a potential respondent. All interview breakoffs and refusals were re-contacted at least once in order to attempt to convert them to completed interviews. In each contacted household, interviewers asked to speak with the “youngest male 18 or older who is at home.” If there is no eligible man at home, interviewers asked to speak with “the oldest woman 18 or older who is at home.” This systematic respondent selection technique has been shown empirically to produce samples that closely mirror the population in terms of age and gender.
Non-response in telephone interview surveys produces some known biases in survey-derived estimates because participation tends to vary for different subgroups of the population, and these subgroups are likely to vary also on questions of substantive interest. In order to compensate for these known biases, the sample data are weighted in analysis.
The demographic weighting parameters are derived from a special analysis of the most recently available Annual Social & Economic Supplement data from the Census Bureau (March 2003). This analysis produced population parameters for the demographic characteristics of households with adults 18 or older, which are then compared with the sample characteristics to construct sample weights. The analysis only included households in the continental United States that contain a telephone. The weights are derived using an iterative technique that simultaneously balances the distributions of all weighting parameters.