This article provides a brief overview of key trends in the survey research to address the nonresponse challenge. Noteworthy are efforts to develop new quality measures and to combine several data sources to enhance either the data collection process or the quality of resulting survey estimates. Mixtures of survey data collection modes and less burdensome survey designs are additional steps taken by survey researchers to address nonresponse.
AbowdJohn M.LaneJulia I.2004. New approaches to confidentiality protection: Synthetic data, remote access and research data centers. U.S. Census Bureau Technical Paper TP-2004-03, Washington, DC.
2.
AitkenAlexisHörngrenJanJonesNiaLewisDanielZilhãoMaria J.2004. Handbook on improving quality by analysis of process variables. Luxembourg: Publications Office of the European Union.
3.
AndridgeRebecca R.LittleRoderick J. A.2011. Proxy pattern-mixture analysis for survey nonresponse. Journal of Official Statistics27 (2):153–80.
4.
BatesNancyDahlhamerJamesSingerEleanor2008. Privacy concerns, too busy, or just not interested: Using doorstep concerns to predict survey nonresponse. Journal of Official Statistics24 (4): 591–612.
5.
BiemerPaul P.LybergLars E.2003. Introduction to survey quality. Hoboken, NJ: John Wiley.
6.
BusheryJohnMcGovernPamelaMarckPaul S.HoganHoward R.2006. Quality audits at the U.S. Census Bureau. In Proceedings of the European conference on quality in survey statistics. Newport, CT: Office for National Statistics.
7.
CampanelliPamelaSturgisPatriciaPurdonSusan1997. Can you hear me knocking: An investigation into the impact of interviewers on response rates. London: Social and Community Planning Research.
8.
CollinsLinda M.MurphySusan A.BiermanKaren L.2004. A conceptual framework for adaptive preventive interventions. Prevention Science5 (3): 185–96.
9.
CouperMick P.1998. Measuring survey quality in a CASIC environment. In JSM Proceedings, 41–49. Alexandria, VA: American Statistical Association.
10.
CouperMick P.LybergLars E.2005. The use of paradata in survey research. In Proceedings of the 55th session of the International Statistical Institute. Sydney, Australia: International Statistical Institute.
11.
De LeeuwEdith D.2005. To mix or not to mix data collection modes in surveys. Journal of Official Statistics21 (2): 233–55.
12.
GonzalezJeffreyEltingeJohn2008. Adaptive matrix sampling for the consumer expenditure interview survey. Paper presented at the Joint Statistical Meeting, American Statistical Association, Denver, CO. Available from http://www.bls.gov/osmr/pdf/st080060.pdf (accessed 14 May 2012).
13.
GriffinDeborah H.FischerDonald P.MorganMichael T.2001. Testing an Internet response option for the American Community Survey. Paper presented at the annual conference of the American Association for Public Opinion Research, Montreal, Quebec, Canada.
14.
GriffinDeborah H.ObenskiSally M.2002. Meeting 21st century demographic needs: Implementing the American Community Survey, Report 2: Demonstrating survey quality. Washington, DC: U.S. Census Bureau.
15.
GrovesRobert2006. Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly70 (5): 646–75.
16.
GrovesRobertHeeringaSteve2006. Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society, Series A169 (3): 439–57.
17.
IannacchioneVincent G.2011. Research synthesis: The changing role of address-based sampling in survey research. Public Opinion Quarterly75 (3): 556–75.
18.
KingSusan L.ChopovaBorianaEdgarJenniferGonzalezJeffrey M.McGrathDave E.TanLucilla2009. Assessing nonresponse bias in the Consumer Expenditure Interview Survey. In JSM Proceedings, 1808–16. Alexandria, VA: American Statistical Association.
19.
KreuterFraukeKohlerUlrich2009. Analyzing contact sequences in call record data: Potential and limitation of sequence indicators for nonresponse adjustment in the European Social Survey. Journal of Official Statistics25 (2): 203–26.
20.
KreuterFraukeMüllerGerritTrappmannMark2010. Nonresponse and measurement error in employment research. Public Opinion Quarterly74 (5): 880–906.
21.
KreuterFraukeOlsonKristenWagnerJamesYanTingEzzati-RiceTrenaCasas-CorderoCarolinaLemayMichaelPeytchevAndyGrovesRobertRaghunathanTrivellore2010. Using proxy measures and other correlates of survey outcomes to adjust for nonresponse: Examples from multiple surveys. Journal of the Royal Statistical Society, Series A173 (2): 389–407.
22.
LepkowskiJamesAxinnWilliamKirgisNicoleWestBrady T.NdiayeShonda KrugerMosherWilliamGrovesRobert2010. Use of paradata in a responsive design framework to manage a field data collection. NSFG Survey Methodology Working Papers Report 10-012, Atlanta, GA.
23.
LinkMichaelMokdadAli2005. Use of alternative modes for health surveillance surveys: Results from a web/mail/telephone experiment. Epidemiology16 (3): 701–4.
24.
LittleRoderick J. A.RubinDonald B.2002. Statistical analysis with missing data. 2nd ed.New York, NY: John Wiley.
25.
LohrSharon1999. Sampling: Design and analysis. Pacific Grove, CA: Duxbury.
26.
LundquistPeterSärndalCarl-Erik2012. Aspects of responsive design for the Swedish Living Conditions Survey. Research and Development Methodology Reports 2012:1. Stockholm: Statistics Sweden.
27.
LybergLarsBiemerPaulCollinsMartinDe LeeuwEdithDippoCathrynSchwarzNorbertTrewinDennis1997. Survey measurement and process quality. New York, NY: John Wiley.
28.
MaitlandAaronCasas-CorderoCarolinaKreuterFrauke2009. An evaluation of nonresponse bias using paradata from a health survey. In JSM Proceedings, 370–78. Alexandria, VA: American Statistical Association.
29.
MontgomeryDouglas C.2005. Introduction to statistical quality control. New York, NY: John Wiley.
30.
MorgansteinDavidHansenMorris1990. Survey operations processes: The key to quality improvement. In Data quality control. Theory and pragmatics, eds. LiepinksGunar E.UppuluriV. R. R., 91–104. New York, NY: Marcel Dekker.
31.
MorgansteinDavidMarkerDavid A.1997. Continuous quality improvement in statistical agencies. In Survey measurement and process quality, eds. LybergLars E.BiemerPaulCollinsMartinDe LeeuwEdith D.DippoCathrynSchwarzNorbertTrewinDenis, 475–500. New York, NY: John Wiley.
32.
MudrykWalterBougieBobXieHansheng2002. Some guidelines for data analysis in quality control. Statistics Canada Technical Report. Ottowa, Ontario, Canada: Statistics Canada.
33.
RaghunathanTrivellore E.ReiterJerome P.RubinDonald B.2003. Multiple imputation for statistical disclosure limitation. Journal of Official Statistics19 (1): 1–16.
34.
RaghunathanTrivelloreXieDaweiSchenkerNathanielParsonsVan L.DavisWilliam W.DoddKevin W.FeuerEric J.2007. Combining information from two surveys to estimate county-level prevalence rates of cancer risk factors and screening. Journal of the American Statistical Association102 (478): 474–86.
35.
RubinDonald1987. Multiple imputation for nonresponse in surveys. New York, NY: John Wiley.
36.
SakshaugJoseph W.Mick P.CouperOfstedalMary Beth2010. Characteristics of physical measurement consent in a population-based survey of older adults. Medical Care48 (1): 64–71.
37.
SärndalCarl-ErikLundströmSixten2008. Assessing auxiliary vectors for control of nonresponse bias in the calibration estimator. Journal of Official Statistics34 (2): 167–91.
38.
SchmuckerAlexandraHuberMartina2009. Linking process generated employment data with survey data: An assessment of the linkage and response quality. In FDZ Methodenreport 5/2009. Nuremberg, Germany: Institut für Arbeitsmarkt und Berufsforschung.
39.
SchoutenBarryCobbenFannieBethlehemJelke2009. Indicators for the representativeness of survey response. Survey Methodology35 (1): 101–13.
40.
ShewhartWalter A.1931. Economic control of quality of manufactured product. New York, NY: Van Nostrand.
41.
SmithTom W.2011. The report of the international workshop of using multi-level data from sample frames, auxiliary databases, paradata and related sources to detect and adjust for nonresponse bias in surveys. International Journal of Public Opinion Research23 (3): 389–402.
42.
Statistics Sweden. 2004. Quality assurance of interview work at Statistics Sweden. Project Report SÄK. Stockholm: Statistics Sweden (in Swedish).
43.
WagnerJames.2008. Adaptive survey design to reduce nonresponse bias. PhD diss., Institute for Social Research, University of Michigan, Ann Arbor.
44.
WagnerJames.2010. The fraction of missing information as a tool for monitoring the quality of survey data. Public Opinion Quarterly74 (2): 223–43.
45.
WestBrady.2011. The error properties of interviewer observations and their implications for nonresponse adjustment of survey estimates. PhD diss., Institute for Social Research, University of Michigan, Ann Arbor.
