Box B: Technical Notes
Data presented in this report are taken from the October 2009 Omnibus Household Survey conducted by the Bureau of Transportation Statistics. The target population is the U.S. noninstitutionalized adult population (18 years or older). Results are based on 1,081 cases; these persons were randomly selected from households using a list-assisted random digit dialing (RDD) methodology.
The findings summarized in this report are based on the sample of households who voluntarily responded to the survey. Respondents were randomly identified for selection as survey respondents among those who had a landline phone; approximately 44 percent of those contacted agreed to participate. As a result, the sample estimates may differ somewhat from the 100-percent figures that would have been obtained if all housing units in the United States and people within those housing units had been interviewed using the same questionnaires, instructions, interviewer, and so forth. The sample estimates also likely differ from the values that would have been obtained from different samples of housing units and people within those housing units.
In addition to the variability that arises from the sampling procedures, both sample data and complete enumeration data are subject to nonsampling error. Nonsampling error may be introduced during any of the various complex operations used to collect and process data. Such errors may include: not enumerating every household or every person in the population, failing to obtain all required information from the respondents, obtaining incorrect or inconsistent information, and recording information incorrectly. In addition, errors can occur during the field review of the interviewers' work, during clerical handling of the Omnibus questionnaires, or during the electronic processing of the questionnaires.
Nonsampling error may affect the data in two ways:
- errors that are introduced randomly will increase the variability of data and, therefore, should be reflected in the standard errors; and
- errors that tend to be consistent in one direction will bias both sample and complete enumeration data in that direction. For example, if respondents consistently tend to underreport their incomes, then the resulting estimates of households or families by income category will tend to be understated for the higher income categories and overstated for the lower income categories.