Adjustments for non-telephone bias in random-digit-dialling surveys

Telephone surveys are widely used in the U.S.A. for the study of health-related topics. They are subject to 'coverage bias' because they cannot sample households that do not have telephones. Although only around 5 per cent of households do not have a telephone, rates of telephone coverage show substantial variation by geography, demographic factors and socio-economic factors. In particular, lack of telephone service is more common among households that contain ethnic and racial minorities or that have lower socio-economic status with fewer opportunities for access to medical care and poorer health outcomes. Thus, failure to adequately account for households without telephones in health surveys may yield estimates of health outcomes that are misleading, particularly in states with at least moderate telephone non-coverage. The dynamic nature of the population of households without telephones offers a way of accounting for such households in telephone surveys. At any given time the population of telephone households includes households that have had a break or interruption in telephone service. Empirical results strongly suggest that these households are very similar to households that have never had telephone service. Thus, sampled households that report having had an interruption in telephone service may be used also to represent the portion of the population that has never had telephone service. This strategy can lead to a reduction in non-coverage bias in random-digit-dialling surveys. This paper presents two methods of adjusting for non-coverage of non-telephone households. The effectiveness of these methods is examined using data from the National Health Interview Survey. The interruption-in-telephone-service methods reduce non-coverage bias and can also result in a lower mean squared error. The application of the interruption-in-telephone-service methods to the National Immunization Survey is also discussed. This survey produces estimates for the 50 states and 28 urban areas. The interruption-in-telephone-service estimates tend be slightly lower than estimates resulting from poststratification and from another non-coverage adjustment method. The results suggest that the reduction in bias is greatest for variables that are highly correlated with the presence or absence of telephone service.