May 28, 2006

Home Broadband Adoption 2006

Methodology

This report is based on the findings of two daily tracking surveys on Americans’ use of the internet. The first was by Princeton Survey Research Associates International between November 29 to December 31, 2005, among a sample of 3,011 adults, 18 and older.  The material in this report dealing with content creation and the attitudes of people towards broadband emerged from this survey. 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 1.9 percentage points.  For results based Internet users (n=1,931), the margin of sampling error is plus or minus 2.4 percentage points.  In addition to sampling error, question wording and practical difficulties in conducting telephone surveys may introduce some error or bias into the findings of opinion polls. The response rate for the December 2005 survey was 28.6%.

The second survey was conducted by Princeton Survey Research Associates International between February 15 to April 6, 2006, among a sample of 4,001 adults, 18 and older.  The material in this report dealing with broadband adoption for various demographic groups emerged from this survey. For results based on the total sample, one can say with 95% confidence that the error attributable to sampling is plus or minus 1.7 percentage points.  For results based Internet users (n=2,822), the margin of sampling error is plus or minus 2.0 percentage points.  In addition to sampling error, question wording and practical difficulties in conducting telephone surveys may introduce some error or bias into the findings of opinion polls. The response rate for the February to April survey was 29.8%.

The sample for these surveys 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 numbers). The design of the sample achieves 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.

New sample was released daily and was kept in the field for at least five days. The sample was released in replicates, which are representative subsamples of the larger population. This ensures that complete call procedures were followed for the entire sample.  At least 10 attempts were made to complete an interview at sampled households. The calls were staggered over times of day and days of the week to maximize the chances of making contact with a potential respondent. Each household received at least one daytime call in an attempt to find someone at home.  In each contacted household, interviewers asked to speak with the youngest male currently at home. If no male was available, interviewers asked to speak with the oldest female at home. This systematic respondent selection technique has been shown to produce samples that closely mirror the population in terms of age and gender.  All interviews completed on any given day were considered to be the final sample for that day.

Non-response in telephone interviews 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 Annual Social and Economic Supplement (March 2005). This analysis produces population parameters for the demographic characteristics of adults age 18 or older, living in households that contain a telephone. These parameters are then compared with the sample characteristics to construct sample weights. The weights are derived using an iterative technique that simultaneously balances the distribution of all weighting parameters.