A second issue related to conducting RDD surveys of low-income populations is the ability to actually find and oversample this group. Screening for persons of low-income has been found to have considerable error. This has been assessed when comparing the poverty status reported on the initial screener and the income reported when using more extensive questions in the longer, extended interview. For example, on the NSAF approximately 10 to 15 percent of those who report being below 200 percent of poverty on the longer interview initially tell the screener they are above this mark. Alternatively, 20 to 30 percent of those reporting themselves as above 200 percent of poverty on the extended interview initially screen in as above this mark (Cantor and Wang, 1998). Similar patterns have been observed for in-person surveys, although the rates do not seem to be as extreme. This reduces the overall efficiency of the sample design. This, in turn, requires increasing sample sizes to achieve the desired level of precision.
To date, the problem has not had a clear solution. In-person surveys have developed more extensive screening interviews to allow predicting income status at the point of the screener (Moeller and Mathiowetz, 1994). This approach also might be taken for RDD screeners, although there is less opportunity to ask the types of questions that are needed to predict income. For example, asking detailed household rosters, or collecting information on jobs or material possessions likely would reduce the screener response rate.
A second issue related to sample design on an RDD survey is the coverage of low-income households. Although only 6 percent of the national population is estimated to be without a telephone (Thornberry and Massey, 1988), about 30 percent of those under poverty are estimated to be in this state. For an RDD survey of a low-income populations, therefore, it is important to decide how coverage issues will be approached. One very expensive approach would be to introduce an area frame into the design. This would include screening for nontelephone households in person and then conducting the extended interviews either in person or over the telephone.(4)
Over the past few years, a new method, based on an imputation method, has been tested that does not require doing in-person interviews (Keeter, 1995). The premise of the method is based on the idea that for a certain segment of the population, having telephone service is a dynamic, rather than stable, characteristic. Consequently, many of the people who do not have service at one point in time may have service shortly thereafter. This implies that one might be able to use persons that have a telephone, but report interrupted service, as proxies for those who do not have telephones at the time the survey is being conducted. Based on this idea, telephone surveys increasingly are including a question that asks respondents if they have had any interruptions in their telephone service over an extended period of time (e.g., past 12 months). If there was an interruption, they are asked how long they did not have service. This information is used in the development of the survey weights. Those reporting significant interruptions of service are used as proxies for persons without a telephone.
Recent evaluations of this method as a complete substitute for actually conducting in-person interviews has shown some promise (Flores-Cervantes et al., 1999). Initial analysis has shown that the use of these questions significantly reduces the bias for key income and other well-being measures when compared to estimates that use in-person interviewing. This is not always the case, however. For certain statistics and certain low income subgroups, the properties of the estimator are unstable. This may be due, in part, to developing better weighting strategies than currently employed. Nonetheless, the use of these questions seems to offer a solution that, given the huge expense involved with doing in-person interviews, may offer significant advantages.
The use of this method also may be of interest to those conducting telephone surveys with persons from a list of welfare clients. Rather than being viewed as a way to reduce coverage error, however, they could be used when trying to impute missing data for high nonresponse rates.