Final data are weighted using demographic information from the U.S. Census to adjust for sampling and non-sampling deviations from population values. Until 2008 ABC News used a cell-based weighting system in which respondents were classified into one of 48 or 32 cells (depending on sample size) based on their age, race, sex and education; weights were assigned so the proportion in each cell matched the Census Bureau’s most recent Current Population Survey. To achieve greater consistency and reduce the chance of large weights, ABC News in 2007 tested and evaluated iterative weighting, commonly known as raking or rim weighting, in which the sample is weighted sequentially to Census targets one variable at a time, continuing until the optimum distribution across variables (again, age, race/ethnicity, sex and education) is achieved. ABC News adopted rim weighting in January 2008. Precision of race/ethnicity weights was enhanced in April 2013, at the same time Spanish interviewing was added. Weights are capped at lows of 0.2 and highs of 6.
In procedures since the start of the dual-frame design, cell-only and landline samples first are weighted by Census region to their respective proportions of the population (per NHIS cell-only estimates). The combined sample is then rim-weighted to full-population Census parameters for age, race/ethnicity, sex and education. A post-weight is applied to the cell-only sample if needed to correct its final proportion within the full sample.
Surveys commonly are weighted to the number of telephone lines in each respondent’s home to adjust for the higher probability of selection of multiple-line households. ABC News has studied the effect of such weighting (Merkle & Langer, Public Opinion Quarterly, Spring 2008) concluding that it carries the risk of distortion, and, when done properly, has no meaningful impact on the data. ABC News polls therefore are not weighted to the number of household phone lines.
Poll results may deviate from full population values because they rely on a sample rather than a census of the full population. Sampling error can be calculated when probability sampling methods, such as those described here, are employed, using the standard formula (at the 95 percent confidence level) of (SQRT(.25/sample size))*1.96, plus adjustment for design effects. There can be other sources of differences in polls, such as question wording and order and systematic noncoverage or selection bias.
As a function of sample size, sampling error is higher for subgroups. We analyze subgroups only as small as 100 cases (or very near it). See our fuller description of sampling error here and our online margin-of-error calculator here.
A survey’s response rates represents its contact rate (the number of households reached out of total telephone numbers dialed, excluding an estimate of nonworking and business numbers) multiplied by its cooperation rate (the number of individuals who complete interviews out of total households reached).