Using Community-Level Correlates to Evaluate Nonresponse Effects in a Telephone Survey
TIMOTHY P. JOHNSON, YOUNG IK CHO and ALLYSON L. HOLBROOK are with the Survey Research Laboratory, and richard t. campbell is with the Institute for Health Policy Research, all at the University of Illinois at Chicago.
Address correspondence to Timothy Johnson; e-mail: timj{at}uic.edu.
Understanding the relationship between nonresponse processes and key research variables is central to evaluating if and how nonresponse introduces bias into survey estimates. In most telephone surveys, however, little information is available with which to estimate these effects. We report a procedure for examining the potential effects of nonresponse via analyses that (1) investigate the linkages between community-level (zip code) variables and survey nonresponse and (2) examine the associations between these community-level variables and key survey measures. We demonstrate these procedures using hierarchical modeling to analyze data from a state-wide telephone survey in Illinois. One zip code–level indicator of concentrated disadvantage—the percentage of the population below poverty level—was found to be positively associated with nonresponse and, among respondents, with both current physical disability status and lack of health insurance coverage, suggesting that both may have been underestimated in this survey. This inexpensive approach has the potential of enabling researchers to routinely evaluate nonresponse effects in their survey data.