Random digit dialing in Chicago CARDIA: comparison of individuals with unlisted and listed telephone numbers

Am J Epidemiol. 1992 Mar 15;135(6):697-709. doi: 10.1093/oxfordjournals.aje.a116349.

Abstract

Young adult blacks and whites aged 18-30 years of both low and high educational levels were recruited through random digit dialing to participate in the Chicago, Illinois, portion of a longitudinal study, Coronary Artery Risk Development in Young Adults (CARDIA). Overall, 31% of randomly selected persons eligible to participate had unlisted telephone numbers--about 50% of black men and women and 11% and 17% of white men and women, respectively. There was no difference in proportions of numbers unlisted by educational level, except for white men, who were more likely to have unlisted numbers at a low educational level than at a high educational level. There was no consistent pattern of differences in rates of participation across race, sex, or education subgroups for unlisted and listed numbers, and there were no significant differences for selected health measures, except smoking. The findings suggest that in Chicago, there is a potential bias in estimates of sociodemographic characteristics from the exclusion of unlisted numbers, but it is likely to be insignificant if recruitment is stratified according to race, sex, and education. Within strata, there was little bias with respect to the attributes measured. Ideally, to guard against possible bias, random digit dialing is recommended as the preferred way to select a representative population-based sample.

Publication types

  • Multicenter Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Chicago / epidemiology
  • Coronary Disease / epidemiology*
  • Educational Status
  • Employment / statistics & numerical data
  • Female
  • Health Behavior
  • Humans
  • Longitudinal Studies
  • Male
  • Marriage / statistics & numerical data
  • Racial Groups
  • Random Allocation*
  • Research Design / standards*
  • Risk Factors
  • Selection Bias*
  • Sex Factors
  • Smoking / epidemiology
  • Telephone / statistics & numerical data*