Nonresponse Rates and Nonresponse Bias in Household Surveys
ROBERT M. GROVES is professor and director, University of Michigan Survey Research Center, and research professor at the Joint Program in Survey Methodology. Support from the National Science Foundation (SES-0207435) is gratefully acknowledged. Ashley Bowers, Sungwoo Lim, and Emilia Peytcheva assisted in the compilation of the prior studies on nonresponse bias. The author appreciates comments on an earlier draft from Norman Bradburn, Michael Brick, Mick Couper, Cheryl Eavey, Hermann Habermann, Richard Lempert, Kristen Olson, Emilia Peytcheva, Stanley Presser, Trivellore Raghunathan, Eleanor Singer, Roger Tourangeau, Richard Valliant, and anonymous reviewers. Errors remaining and views expressed are the author’s.
Address correspondence to the author; e-mail: bgroves{at}isr.umich.edu.
Many surveys of the U.S. household population are experiencing higher refusal rates. Nonresponse can, but need not, induce nonresponse bias in survey estimates. Recent empirical findings illustrate cases when the linkage between nonresponse rates and nonresponse biases is absent. Despite this, professional standards continue to urge high response rates. Statistical expressions of nonresponse bias can be translated into causal models to guide hypotheses about when nonresponse causes bias. Alternative designs to measure nonresponse bias exist, providing different but incomplete information about the nature of the bias. A synthesis of research studies estimating nonresponse bias shows the bias often present. A logical question at this moment in history is what advantage probability sample surveys have if they suffer from high nonresponse rates. Since postsurvey adjustment for nonresponse requires auxiliary variables, the answer depends on the nature of the design and the quality of the auxiliary variables.
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