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This Article Full Text Full Text (PDF) All Versions of this Article: AJPH.2008.154161v1 99/10/1811    most recent Submit a response View Shopping Cart Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Lee, S. Articles by Brick, J. M. PubMed PubMed Citation Articles by Lee, S. Articles by Brick, J. M. Related Collections Social Science Surveillance Surveys Other Statistics/Evaluation/Research

Sunghee Lee, PhD, E. Richard Brown, PhD, David Grant, PhD, Thomas R. Belin, PhD and J. Michael Brick, PhD

Sunghee Lee is with the Center for Health Policy Research and the Department of Biostatistics, University of California, Los Angeles. E. Richard Brown is with the Center for Health Policy Research and the Department of Health Services, University of California, Los Angeles. David Grant is with the Center for Health Policy Research, University of California, Los Angeles. Thomas R. Belin is with the Department of Biostatistics, University of California, Los Angeles. J. Michael Brick is with Westat Inc, Rockville, MD.

Correspondence: Correspondence should be sent to Sunghee Lee, PhD, UCLA Center for Health Policy Research, 10960 Wilshire Blvd, Suite 1550, Los Angeles, CA 90024 (e-mail: slee9{at}ucla.edu). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints/Eprints" link.

Objectives. We examined potential nonresponse bias in a large-scale, population-based, random-digit-dialed telephone survey in California and its association with the response rate.

Methods. We used California Health Interview Survey (CHIS) data and US Census data and linked the two data sets at the census tract level. We compared a broad range of neighborhood characteristics of respondents and nonrespondents to CHIS. We projected individual-level nonresponse bias using the neighborhood characteristics.

Results. We found little to no substantial difference in neighborhood characteristics between respondents and nonrespondents. The response propensity of the CHIS sample was similarly distributed across these characteristics. The projected nonresponse bias appeared very small.

Conclusions. The response rate in CHIS did not result in significant nonresponse bias and did not substantially affect the level of data representativeness, and it is not valid to focus on response rates alone in determining the quality of survey data.