Volume 32 - Article 26 | Pages 797–828
Do low survey response rates bias results? Evidence from Japan
|Date received:||10 Nov 2013|
|Date published:||25 Mar 2015|
|Keywords:||family, fertility, Japan, migration, response rates, survey methods|
Background: In developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results.
Objective: The paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community.
Methods: Using a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias.
Results: Evidence of response-rate bias is found for the univariate distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates.
Conclusions: Our results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple univariate distribution than when examining the relationship between variables in a multivariate model.
Comments: The results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.
Ronald R. Rindfuss - University of North Carolina at Chapel Hill, United States of America
Minja K. Choe - East-West Center, United States of America
Noriko O. Tsuya - Keio University, Japan
Larry L. Bumpass - University of Wisconsin–Madison, United States of America
Emi Tamaki - Ritsumeikan University, Japan
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