Results for the survey are based on telephone interviews conducted under the direction of Princeton Survey Research Associates among a nationwide sample of 1,073 adults, 18 years of age or older, during the period December 8-12, 1999. For results based on the total sample, one can say with 95% confidence that the error attributable to sampling and other random effects is plus or minus 3.5 percentage points. For results based on either Form 1 (N=518) or Form 2 (N=555), the sampling error is plus or minus 5 percentage points. For results based on registered voters (N=745), the sampling error is plus or minus 4 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.
REGRESSION ANALYSES IN DETAIL
The extent to which voters’ general opinions about the candidates and awareness of campaign events and issues are related to their support for candidates in the primary elections was analyzed in part with multiple regression analyses. One table in the text presents the unstandardized regression coefficients associated with items included in the analyses. These values are one measure of the relative statistical association between an item and support for a candidate. Items which are not significantly related to support are identified with ns (i.e., not statistically significant).
A separate multiple regression analysis was calculated for each candidate. In addition, the impact of campaign events and issues (e.g., having heard about George W. Bush’s pop quiz or that Bill Brad ley played professional basketball) and the impact of general impressions of the candidates were measured in separate multiple regressions. For each analysis, demographic factors including age, education, sex, race, and region, as well as measures of interest in the campaign, were taken into account by including them as factors in the regression model.
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 were 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 five 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 Census Bureau’s Current Population Survey (March 1999). 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.
