title: Are lower response rates hazardous to your health survey? An analysis of three state telephone health surveys.
abstract: OBJECTIVE
  To examine the impact of response rate variation on survey estimates and costs in three health telephone surveys.
  
  
  METHODS
  Three telephone surveys of noninstitutionalized adults in Minnesota and Oklahoma conducted from 2003 to 2005.
  
  
  METHODS
  We examine differences in demographics and health measures by number of call attempts made before completion of the survey or whether the household initially refused to participate. We compare the point estimates we actually obtained with those we would have obtained with a less aggressive protocol and subsequent lower response rate. We also simulate what the effective sample sizes would have been if less aggressive protocols were followed.
  
  
  RESULTS
  Unweighted bivariate analyses reveal many differences between early completers and those requiring more contacts and between those who initially refused to participate and those who did not. However, after making standard poststratification adjustments, no statistically significant differences were observed in the key health variables we examined between the early responders and the estimates derived from the full reporting sample.
  
  
  CONCLUSIONS
  Our findings demonstrate that for the surveys we examined, larger effective sample sizes (i.e., more statistical power) could have been achieved with the same amount of funding using less aggressive calling protocols. For some studies, money spent on aggressively pursuing high response rates could be better used to increase statistical power and/or to directly examine nonresponse bias.
authors.affiliation: University of Chicago, 55 E. Monroe, Suite 3000, Chicago, IL 60603, USA. davern-michael@norc.org
authors.forename: Michael
authors.initials: M
authors.lastname: Davern
authors.forename: Donna
authors.initials: D
authors.lastname: McAlpine
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Beebe
authors.forename: Jeanette
authors.initials: J
authors.lastname: Ziegenfuss
authors.forename: Todd
authors.initials: T
authors.lastname: Rockwood
authors.forename: Kathleen Thiede
authors.initials: KT
authors.lastname: Call
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015982:Bias; D005260:Female; D019538:Health Care Surveys / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data*; D006297:Health Services Accessibility / Q000706:statistics & numerical data; D006306:Health Surveys; D006801:Humans; D019458:Insurance Coverage / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D008910:Minnesota; D015999:Multivariate Analysis; D020380:Needs Assessment / Q000706:statistics & numerical data; D009825:Oklahoma; D010342:Patient Acceptance of Health Care / Q000523:psychology / Q000706:statistics & numerical data; D010352:Patient Dropouts / Q000523:psychology / Q000706:statistics & numerical data; D012107:Research Design; D035842:Research Subjects / Q000523:psychology / Q000600:supply & distribution*; D018401:Sample Size; D013689:Telephone